Table of Contents
Overview
Key Processes in Drug Discovery
The drug discovery process is a complex, multi-stage journey aimed at identifying and developing new pharmaceutical therapies. It begins with early-stage research and culminates in clinical trials before a potential drug can be approved for human use.[4.1] The process involves identifying a disease to be treated, understanding its unmet medical needs, and selecting a druggable molecular target.[5.1] Historically, drug discovery has evolved significantly. The late 1800s marked a shift from natural extraction to scientific techniques, leading to large-scale synthesis and testing of drugs.[3.1] This evolution continued into the early 20th century with the emergence of antibiotics as a major breakthrough in medical treatment, significantly influencing the management of infectious diseases.[17.1] In contemporary drug discovery, advancements in genomics and proteomics have transformed the identification of drug targets by enabling precise molecular analysis of diseases. Genomic approaches facilitate the identification of genetic variations, mutations, and expression patterns associated with various pathologies, while proteomic techniques reveal alterations in protein abundance, modifications, and interactions.[11.1] The integration of multi-omics approaches, combining data from genomics, transcriptomics, proteomics, metabolomics, and other omics, is essential for unraveling the complexities of disease biology and improving therapeutic outcomes.[15.1] This underscores the critical role of genetic and genomic knowledge in facilitating target identification and advancing drug leads to candidates for preclinical and clinical studies.[12.1] The drug discovery and development process is a multifaceted journey overseen by the Food and Drug Administration (FDA), requiring collaboration among drug sponsors, clinical researchers, and regulatory authorities.[10.1] It is typically described as linear, consisting of discrete scientific stages that correspond to safety and efficacy benchmarks established by regulatory bodies such as the FDA.[9.1] Adapting to changes in global regulations and standards is crucial, as new guidelines significantly impact how drugs are developed, tested, and approved.[8.1] Consequently, stakeholders must stay informed about these evolving regulatory frameworks to ensure that development processes align with current standards and meet the regulatory requirements necessary for approval and market entry.[8.1]In this section:
History
Evolution of Drug Discovery Techniques
The evolution of drug discovery techniques can be categorized into three main periods, each marked by significant advancements and methodologies. The first period, which began in the nineteenth century, was characterized by a reliance on serendipity, where medicinal chemists often discovered drugs by chance rather than through systematic approaches. Notable examples of serendipitous discoveries from this era include penicillin and chlorpromazine, which were found while searching for other compounds.[49.1] The evolution of drug discovery techniques has undergone significant transformations since the late 1800s, transitioning from serendipitous discoveries to systematic scientific methodologies. The first notable period in drug discovery relied heavily on the chance findings of medicinal chemists, while the early twentieth century marked a pivotal shift with the emergence of new drug structures, particularly antibiotics, which contributed to a new era in drug discovery.[2.1] Over the past century, antibiotics have drastically changed modern medicine and extended the average human lifespan by 23 years.[48.1] However, this progress has been accompanied by a gradual decline in antibiotic discovery and the evolution of drug resistance in many human pathogens, leading to the current antimicrobial resistance crisis.[48.1] Despite these challenges, the future of antibiotic discovery appears promising, as new technologies such as genome mining and editing are being deployed to discover new natural products with diverse bioactivities.[48.1] The evolution of drug discovery techniques has been profoundly shaped by advancements in multi-omics technologies, artificial intelligence (AI), and machine learning (ML) in recent years. Multi-omics is a novel approach that systematically studies biology by integrating and analyzing data from various omics levels, including genomics, transcriptomics, proteomics, and metabolomics, in an unbiased manner.[44.1] This integration has significantly improved the identification of drug targets and the personalization of therapies, making the drug discovery process more accurate, less time-consuming, and cost-effective compared to conventional methods.[43.1] Furthermore, the combination of network pharmacology with omics technologies has established a robust framework for drug research, particularly in cancer treatment, by providing essential insights into the mechanisms of drug action and the interactions within biological systems.[42.1] These advancements have laid a solid foundation for future developments in drug discovery, enhancing the overall efficiency and effectiveness of therapeutic interventions.Recent Advancements
Role of Artificial Intelligence
The integration of Artificial Intelligence (AI) in drug discovery is rapidly transforming the pharmaceutical industry, significantly enhancing the efficiency and accuracy of the drug development process. AI applications are particularly impactful in the early stages of drug discovery, where they facilitate the identification of novel therapeutic targets and the design of therapeutic molecules. By leveraging large datasets, including electronic health records and genomic data, AI enables faster identification of drug targets and streamlines clinical trials, ultimately personalizing treatment regimens for individual patients.[88.1] Recent advancements in AI have introduced innovative methodologies that improve the drug discovery process. For instance, AI-based approaches can enhance the predictive power of drug efficacy and safety, which is crucial given the high costs and long timelines associated with traditional drug development.[86.1] Moreover, AI's ability to analyze complex biological data can mitigate challenges related to the intricate biology of diseases such as cancer, where approximately 90% of drug candidates fail to reach the market due to insufficient efficacy and safety levels.[86.1] The potential of AI in drug discovery is further exemplified by its role in optimizing molecule design and enhancing clinical trial efficiency. AI-driven frameworks, such as PaccMann, have been developed to predict cancer cell sensitivity to various compounds by integrating molecular structures, gene expression profiles, and protein interaction data.[86.1] This integration not only accelerates the identification of effective treatments but also supports the development of personalized medicine strategies. Despite these advancements, the application of AI in drug discovery faces several challenges. Issues such as the need for robust data-sharing mechanisms and comprehensive intellectual property protections for algorithms remain significant hurdles.[85.1] Additionally, the reliance on large datasets raises concerns regarding the privacy and security of sensitive personal health information.[88.1] Nevertheless, the ongoing evolution of AI technologies, including the use of explainable AI and data augmentation, holds promise for overcoming these challenges and further revolutionizing the drug discovery landscape.[87.1] As the pharmaceutical industry continues to embrace AI, the future of drug discovery appears poised for transformative changes that could lead to more effective medications and improved patient outcomes.[87.1]Innovations in High-Throughput Screening
Recent advancements in drug discovery have significantly enhanced the methodologies employed in high-throughput screening (HTS), which is crucial for identifying potential drug candidates efficiently. The integration of advanced technologies, such as artificial intelligence (AI), has transformed the landscape of drug regulation and screening processes. AI applications in drug regulation are expanding, particularly in safety surveillance and workflow optimization, which are essential for improving the efficiency of HTS.[118.1] Moreover, the FDA's Patient-Focused Drug Development (PFDD) initiative exemplifies a shift towards incorporating patient-reported outcomes into the drug development process, thereby enhancing the relevance of HTS results to actual patient needs.[115.1] This initiative underscores the importance of patient advocacy and engagement, which are increasingly recognized as vital components in the drug discovery process. To further adapt to rapid advancements in drug discovery technologies, regulatory agencies are encouraged to implement strategies that foster interdisciplinary collaboration. Such collaboration can harness the full potential of AI and other innovative technologies, ensuring that they are effectively integrated into regulatory practices without compromising patient safety.[118.1] Additionally, recommendations from the President's Council of Advisors on Science and Technology emphasize the need for industry, academia, and government to work together to enhance drug efficacy and safety while reducing clinical trial costs and regulatory uncertainties.[117.1]In this section:
Drug Development Process
Stages of Drug Development
The drug development process consists of several critical stages that ensure the safety and efficacy of new medications. Initially, the process begins with a pre-discovery stage, where basic research is conducted to understand disease mechanisms and propose potential drug targets, such as specific proteins.[121.1] Following this, the drug discovery stage involves the identification of molecules or therapeutic strategies that can interfere with or cure the disease, or at least alleviate its symptoms.[121.1] The process of drug development is a multifaceted journey that typically encompasses five main stages: 1) a pre-discovery stage, 2) drug discovery, 3) preclinical development, 4) clinical trials, and 5) reviewing, approval, and post-market monitoring.[121.1] The pre-discovery stage involves basic research aimed at understanding the mechanisms that lead to diseases and proposing potential therapeutic targets, such as proteins.[121.1] During the drug discovery phase, scientists search for molecules, which can be categorized into two main families: small molecules and biologics, or explore other therapeutic strategies that may interfere with, cure, or alleviate the symptoms of the disease in question.[121.1] Following this, the preclinical development stage focuses on clarifying the mode of action of drug candidates, investigating potential toxicity, validating efficacy through various in vitro and in vivo models, and beginning formulation evaluations.[121.1] Once preclinical development is complete, the drug candidates enter the clinical stage, where they are tested in humans through clinical trials.[121.1] These clinical trials are divided into three phases: Phase 1 trials primarily assess the drug's safety profile, determine a safe dosage range, and identify any side effects.[121.1] Phase 2 trials further evaluate the drug's efficacy and safety in a larger group of participants, while Phase 3 trials are large-scale studies designed to confirm the drug's efficacy, monitor side effects, and compare it to existing treatments or a placebo.[121.1] After the completion of these clinical trials, the drug undergoes a review process, during which it is evaluated for safety and efficacy before it can be approved for market use.[121.1] The drug development process is a structured journey that consists of five distinct stages: 1) Discovery and Development, 2) Preclinical Research, 3) Clinical Trials, 4) FDA Drug Review, and 5) FDA Post-Market Safety Monitoring.[122.1] Before a drug enters the clinical trial phase, a significant amount of preliminary research is conducted to ensure its potential efficacy and safety. Clinical trials are divided into three phases: Phase 1 trials primarily focus on assessing the drug's safety profile, determining a safe dosage range, and identifying any side effects. Phase 2 trials further evaluate the drug's safety and begin to assess its efficacy, while Phase 3 trials are large-scale studies designed to confirm the drug's efficacy, monitor its side effects, and compare it to existing treatments or a placebo.[122.1] After successfully completing these trials, the drug advances to the FDA review stage, where a team of experts evaluates the safety and efficacy findings from the clinical trials. If the drug is approved, it then enters the post-market safety monitoring phase, which is essential for ongoing assessment of its safety in the general population.[122.1] This multi-stage process is crucial for ensuring that new drugs are both safe and effective before they are made available to the public.Regulatory Considerations
The regulatory framework for drug development presents significant challenges, primarily due to the stringent requirements set by agencies like the FDA. These requirements contribute to the lengthy and costly process of bringing a biotherapeutic from concept to market, often taking over a decade and costing more than $1 billion, with no guarantee of FDA approval despite substantial investment.[128.1] The complexity of navigating these regulations necessitates a thorough understanding of the guidelines to ensure compliance and successful drug approval.[131.1] Recent trends show the FDA's approval of over 150 oncology drugs, including 42 novel therapeutics between 2021 and 2023, underscoring the competitive nature of the drug development landscape.[129.1] To effectively address regulatory challenges, developers must adopt data-driven strategies that streamline the development process while adhering to regulatory standards.[130.1] Ensuring diversity in clinical trials is crucial for the safety and efficacy of drugs across various demographics. However, barriers persist that hinder the participation of underrepresented groups.[133.1] Strategies to overcome these barriers include advocating for policies that mandate diverse patient populations and leveraging community partnerships to enhance trust and recruitment efforts.[136.1] The NIH’s Trial Innovation Network (TIN) provides tools to support clinical trials, focusing on increasing participation from marginalized communities and addressing systemic barriers.[136.1] To enhance the validity of clinical trials, employing random treatment assignments and masking treatments are essential practices that improve internal validity.[137.1] Additionally, minimizing exclusion criteria can enhance the external validity of trial results, making them more applicable to the general population.[138.1] By addressing these regulatory considerations and challenges, drug developers can better position their products for successful approval and market entry.Drug Repurposing
Benefits and Challenges
Drug repurposing, also known as drug repositioning, presents notable advantages that enhance its appeal as a strategy in drug discovery. One of the key benefits is the potential for expedited regulatory approvals, as existing drugs come with readily available preclinical, safety, and tolerability data.[166.1] This availability of data not only facilitates faster approvals but also contributes to the cost-effectiveness of drug repurposing. The average cost of repurposing a drug is estimated to be around $300 million, significantly lower than the $2 to $3 billion typically required for the development of a new chemical entity.[166.1] Furthermore, much of the phase I and II clinical work has already been completed for the original indications, which allows for a more efficient exploration of the drug's therapeutic potential for different diseases.[166.1] Overall, drug repurposing not only alters the timeline and cost associated with drug development but also holds promise for identifying treatments for various diseases that currently lack effective therapeutics.[166.1] Despite its advantages, drug repurposing is not without significant challenges. One of the primary hurdles is navigating the complex landscape of intellectual property rights and regulatory requirements, which can complicate the repurposing process.[169.1] Regulatory challenges include demonstrating efficacy for new indications and understanding complex regulatory pathways.[172.1] Additionally, financial hurdles such as securing funding for research and addressing pricing and reimbursement challenges further complicate the efforts of pharmaceutical companies.[172.1] While repurposed drugs may not qualify for new patents, it is noteworthy that about half of 505(b)(2) drugs are associated with patents or other statutory exclusivities, which can influence the financial incentives for companies to engage in repurposing.[171.1] Overall, although drug repurposing presents a promising strategy for developing new therapies with potentially lower risks and costs, the realization of its full potential is hindered by these legal and regulatory barriers.[173.1]Case Studies of Successful Repurposing
Drug repurposing has emerged as a significant strategy in drug discovery, particularly in the context of addressing urgent health challenges. One notable example is the repurposing of fluoroquinolones as anticancer agents. Recent studies have demonstrated that these drugs can exert their anticancer effects through various mechanisms, including the induction of cell cycle arrest at different phases and the triggering of apoptosis by targeting the cancer suppressor gene P53. These findings highlight the potential of fluoroquinolones in cancer therapy, showcasing their promising biological results in this new indication.[196.1] Additionally, drug repurposing has been effectively utilized in the context of cancer therapy, where it serves as a promising strategy for drug discovery. Research has classified drug response patterns of colorectal cancer (CRC) organoids into five prevalent groups, which represent general responses to repurposed drugs. This classification aids in understanding the biological mechanisms at play and supports the selection process for potential candidates in cancer treatment.[197.1] Moreover, targeted mechanism-based methods have been developed to enhance drug repurposing efforts. These methods leverage drug omics data, disease pathways, and protein interaction networks to identify key targets for repurposing drugs. Signature-based approaches utilize gene signatures derived from disease omics data to discover unknown off-target or disease mechanisms, thereby facilitating the identification of effective repurposed drugs.[198.1] In addition to cancer, repurposed drugs have shown promising effects against various conditions, including sepsis-induced organ injury, hepatic disorders, diabetic nephropathy, ulcerative colitis, and immune disorders. The mechanisms underlying these effects often involve the suppression of key signaling pathways, such as STAT3, NF-κB, TLR4, and β-catenin, which in turn leads to a decrease in pro-inflammatory cytokines like TNF-α, IL-1β, and IL-6. This highlights the versatility of repurposed drugs in addressing a range of health issues beyond their original indications.[199.1]Natural Products In Drug Discovery
Contribution of Natural Products
Natural products have historically played a crucial role in drug discovery, serving as a significant source of new lead compounds. Many therapeutic agents currently in use have been derived from natural sources, particularly plants, which have been instrumental in the development of various medications.[207.1] The journey from the discovery of a natural product-based drug to its market launch is complex, involving multiple stages such as initial discovery, drug development, and clinical trials. This process can take approximately ten years and cost over $800 million, largely due to the high number of leads that are discarded throughout the development stages.[209.1] The structural diversity and complexity of natural products contribute to their unique biological activities, which often result from evolutionary adaptations. This diversity allows natural products to exhibit potent bioactivity, making them valuable in pharmacotherapy, especially for conditions like cancer and infectious diseases.[214.1] However, the isolation and characterization of these compounds can be challenging, often requiring extensive purification processes.[214.1] Despite these challenges, advancements in technology, such as improved analytical tools and genome mining, have revitalized interest in natural products for drug discovery, opening new avenues for exploration.[210.1] The modification of bioactive natural products (NPs) is a versatile strategy employed to enhance their pharmacological properties and explore their modes of action, which may lead to the discovery of novel drugs.[213.1] Various medicinal chemistry approaches, including structural optimization, semi-synthesis, prodrug design, and molecular hybridization, are utilized in this context.[212.1] For instance, structural modifications can significantly influence the selectivity of compounds, as demonstrated by the impact of the trifluoromethyl group on the benzene ring in siponimod, which boosted its activity by over 30%.[211.1] The inherent structural diversity and complexity of NPs make them suitable scaffolds for modification, thereby facilitating drug discovery and development.[213.1] Natural products present unique challenges during clinical trials compared to synthetic drugs, primarily due to their inherent variability in composition, which can arise from environmental factors and extraction methods.[214.1] This variability complicates the assessment of their efficacy and safety, necessitating more extensive preclinical and early-phase work before progressing to phase III trials.[216.1] Clinical trials for natural products are considered the gold standard for evaluating the scientific evidence of herbal medicinal products, yet they exhibit distinct characteristics that differ from those of synthetic drugs.[215.1] Despite these challenges, natural products and traditional medicines offer significant advantages in drug development, including abundant clinical experiences and a diverse array of chemical structures and biological activities.[217.1] The historical success of natural products in yielding approved drugs, such as antibiotics and anticancer agents, highlights their enduring relevance in the pharmaceutical landscape.[214.1]Advances in Natural Product Chemistry
The pre-genomic era of natural product discovery primarily utilized a 'top-down' approach, which involved screening biological samples for desirable bioactivities, followed by the isolation and characterization of compounds. However, by the 1990s, this strategy had largely failed to uncover new natural products, as pharmaceutical companies struggled to find innovative leads.[223.1] The integration of genome mining and high-throughput screening has since emerged as a transformative force in the field, enhancing the methodologies used for isolating and characterizing natural products for drug discovery. With the advent of the genomic era, computational genome mining has emerged as a crucial tool in identifying novel natural products that can serve as drug leads. This approach involves the identification of previously uncharacterized natural product biosynthetic gene clusters within sequenced genomes, coupled with the analysis of the enzymes encoded by these clusters and the experimental identification of their products.[222.1] The development of high-throughput sequencing and DNA databases has further enhanced genome mining methods, facilitating the discovery and characterization of natural products.[224.1] Moreover, the combination of genome mining with synthetic biology has provided substantial support in drug discovery efforts, allowing researchers to identify new natural products more efficiently.[225.1] For instance, Yuan et al. have introduced a fully automated, high-throughput platform specifically designed for discovering natural product biosynthetic gene clusters in Streptomyces species, which holds great promise for accelerating the development of pharmaceutical agents across various therapeutic applications.[226.1] Artificial intelligence (AI) and machine learning are also playing pivotal roles in optimizing the processes of genome mining and high-throughput screening. These technologies are increasingly utilized in the research and development of natural product drugs, enhancing critical stages such as target discovery, hit identification, and lead optimization.[231.1] AI-driven approaches have led to advancements in computational drug design, enabling the prediction of biological activities and facilitating the identification of drug candidates from the vast array of natural molecules.[232.1] However, challenges remain, including the need for high-quality datasets to train deep learning algorithms and effective strategies for algorithm validation.[232.1] Recent advancements in natural product chemistry have introduced several methodologies aimed at enhancing the bioavailability of natural products, which is crucial for their effectiveness as therapeutic agents. One of the most established techniques is particle size reduction, which significantly increases the surface area and wettability of hydrophobic drugs, thereby improving their solubility and bioavailability.[234.1] Cyclodextrins have also been recognized for their ability to improve absorption and assist in the design of more effective medicinal products due to their versatility and safety.[236.1] Additionally, the creation of prodrugs, which involves chemically modifying active pharmaceutical ingredients (APIs) to enhance their solubility, permeability, and metabolic stability, represents another promising approach to improving bioavailability.[236.1] Furthermore, novel drug delivery systems have emerged over the past decade, encapsulating active pharmaceutical ingredients into nano-cargo such as phytosomes, liposomes, nanoparticles, and other lipid-based systems, which have proven successful in enhancing the bioavailability of natural products.[239.1] Lastly, studies have shown that spray drying can significantly improve drug absorption and distribution, allowing natural products to exert their target clinical effects more effectively.[237.1]In this section:
Challenges In Drug Discovery
Economic and Time Constraints
The drug discovery process is fraught with significant economic barriers and time constraints that can impede the development of new medications. One of the primary economic challenges is the high cost associated with drug discovery, which has been exacerbated by the complexities of the process, including lengthy timelines and the uncertainty of outcomes. The collective experience of the COVID-19 pandemic has highlighted the need for reform in traditional economic models of drug discovery to better address current and future unmet medical needs.[264.1] Pharmaceutical companies face substantial financial risks, particularly when candidates fail during development. Innovative funding models and partnerships have emerged to mitigate these risks. For instance, the Wellcome Trust's £91 million Seeding Drug Discovery initiative exemplifies a collaborative approach that leverages the strengths of both industry and academia throughout the drug discovery process.[266.1] Additionally, innovative financing models such as high-cost drug mortgages, reinsurance, and patient rebates have been proposed to alleviate the immediate financial burden of specialty drugs on healthcare payers.[267.1] Moreover, the challenges of conducting multi-country clinical trials add to the time constraints faced by pharmaceutical companies. Each country has its own regulatory authority with specific requirements, which can complicate compliance and extend timelines.[255.1] To navigate these complexities effectively, sponsors and contract research organizations (CROs) must adopt strategic approaches that ensure regulatory compliance and streamline the approval process.[255.1] Quality by Design (QbD) is another approach that can help address economic challenges by focusing on the systematic design of drug formulations and manufacturing processes. Companies that implement strict raw material specifications and in-process quality testing have reported a reduction in batch failures, leading to improved product quality and efficiency.[261.1] By addressing issues such as solubility and regulatory compliance, QbD can ultimately reduce costs and accelerate the time to market for new drugs.[261.1]Scientific and Technical Limitations
Drug discovery is a complex and challenging process that involves the comprehensive application of multiple disciplines and knowledge areas. One of the primary challenges in this field is the scientific complexity associated with understanding the biological mechanisms of diseases, which is crucial for identifying effective therapeutic options.[243.1] This process encompasses a variety of scientific fields, including pharmacology, medicinal chemistry, biochemistry, and biology.[246.1] The integration of these disciplines is essential for overcoming the obstacles faced in the development of novel potential medicines through a series of critical research activities.[246.1] The drug discovery process is also characterized by lengthy timelines and high costs, often taking between 8 to 12 years and exceeding £1 billion on average to bring a new drug to market.[244.1] This financial burden is exacerbated by regulatory pressures and the influence of payors on drug pricing, which further complicates the establishment of a reliable revenue stream for pharmaceutical companies.[244.1] Moreover, the inherent uncertainty and complexity of the drug discovery process are compounded by the unknown pathophysiology of many disorders, making target identification particularly challenging.[247.1] The limitations of animal models and the heterogeneity of patient populations further restrict the effectiveness of one-size-fits-all drug approaches, leading to low success rates in clinical trials, where approximately 90% of candidates fail to reach the market.[251.1] Recent advancements in technology, such as multi-omics approaches and artificial intelligence, have emerged as potential solutions to some of these challenges. For instance, multi-omics technologies facilitate drug-target identification by integrating various biological data sources, while AI-driven methods enhance the analysis of complex datasets, improving target identification and predicting druggability.[251.1] However, despite these innovations, much more research and development are needed to fully address the limitations faced in modern drug discovery.[247.1]In this section:
Future Directions
Emerging Technologies
Emerging technologies are significantly transforming the landscape of drug discovery, particularly through the integration of artificial intelligence (AI) and machine learning (ML). These advanced technologies have revamped traditional drug discovery methods by enabling rapid analysis of complex biological data and the identification of new therapeutic targets.[282.1] The combination of AI with advanced imaging technologies, such as high-throughput microscopy and single-cell sequencing, has opened new avenues for discovering and validating drug targets.[282.1] By merging imaging data with AI and deep learning algorithms, researchers can uncover novel drug targets and gain deeper insights into disease mechanisms, ultimately leading to the development of more effective therapies.[282.1] AI's role in drug discovery extends to various domains, including drug characterization, target discovery and validation, small molecule drug design, and the acceleration of clinical trials.[283.1] The predictive capabilities of AI, when synergistically combined with the expertise of human researchers, present opportunities to optimize the drug discovery process and expedite the development of new medications.[285.1] Furthermore, AI facilitates early predictions of drug efficacy and safety, which is crucial in the early stages of drug discovery.[290.1] Despite the promising advancements, the application of AI in drug development faces several challenges. These include the need for robust data-sharing mechanisms and the establishment of comprehensive intellectual property protections for algorithms.[283.1] Ethical considerations also play a significant role, as issues related to data availability, standards, and regulatory guidance can hinder the acceptance of AI tools in drug development.[286.1] Addressing these challenges is essential for harnessing the full potential of AI and ML in improving patient care and advancing medical research.[288.1]References
[2] An historical overview of drug discovery - PubMed — Federal government websites often end in .gov or .mil. official website and that any information you provide is encrypted Save citation to file The first notable period can be traced to the nineteenth century where the basis of drug discovery relied on the serendipity of the medicinal chemists. The second period commenced around the early twentieth century when new drug structures were found, which contributed for a new era of antibiotics discovery. With all the expansion of new technologies and the onset of the "Omics" revolution in the twenty-first century, the third period has kick-started with an increase in biopharmaceutical drugs approved by FDA/EMEA for therapeutic use. PubMed Disclaimer
[3] PDF — Drug discovery and development started to follow scientifi c techniques in the late 1800s. From then on, more and more drugs were discovered, tested, and synthesized in large - scale manufacturing plants, as opposed to the extraction of drug products from natural sources in relatively small batch quantities.
[4] What Are the Key Steps in Drug Discovery Processes? — Key Steps Involved in the Drug Discovery Process A Comprehensive Overview of the Drug Discovery Journey . The drug discovery process is a complex and multi-stage journey that aims to identify and develop new pharmaceutical therapies. It involves a series of stages, from early-stage research to clinical trials, before a potential drug can be approved for human use.
[5] Drug Discovery and Development: An Overview - ScienceDirect — Drug discovery is a process which is intended to identify a small synthetic molecule or a large biomolecule for comprehensive evaluation as a potential drug candidate. Broadly, the modern drug discovery process includes identification of disease to be treated and its unmet medical need, selection of a druggable molecular target and its
[8] Why Regulatory Affairs is Important in Drug Development — Adapting to changes in global regulations and standards is crucial, as new guidelines impact how drugs are developed, tested, and approved. This requires staying abreast of these changes so that development processes align with evolving standards and drugs meet the regulatory requirements for approval and market entry.
[9] Exploring regulatory flexibility to create novel incentives to optimize ... — Drug development is usually described as a linear process with discrete scientific stages corresponding to safety and efficacy benchmarks set by regulations and the administrative bodies that apply them, such as the FDA [Figure 1; e.g., ].
[10] Federal Medication Development Regulation - StatPearls - NCBI Bookshelf — Overseen by the Food and Drug Administration (FDA), the process mandates a collaborative effort among drug sponsors, clinical researchers, and regulatory authorities. Navigating the drug development pipeline, from initial discovery through rigorous investigation, extensive clinical trials, and ultimate FDA approval, is crucial for ensuring the
[11] Target Identification in Drug Discovery: Current Strategies — Advancements in genomics and proteomics have transformed drug target identification by enabling precise molecular analysis of diseases. Genomic approaches identify genetic variations, mutations, and expression patterns associated with pathology, while proteomic techniques reveal alterations in protein abundance, modifications, and interactions.
[12] Advances in Genomics for Drug Development - PMC - PubMed Central (PMC) — Here, we review the contribution of population genomics to target identification, ... Drug development (target identification, advancing drug leads to candidates for preclinical and clinical studies) can be facilitated by genetic and genomic knowledge. ... Although this review does not discuss the impact of genomics in later stages of drug
[15] Emerging trends and hot topics in the application of multi-omics in ... — Drug discovery research, particularly in the realm of multi-omics, is rapidly evolving as scientists strive to unravel the complexities of disease biology and improve therapeutic outcomes , .Multi-omics approaches, integrating data from genomics, transcriptomics, proteomics, metabolomics, and other omics, have become essential for understanding the complex molecular mechanisms of
[17] Antibiotics: past, present and future - ScienceDirect — The introduction of antibiotics into clinical use was arguably the greatest medical breakthrough of the 20th century (Figure 1) .In addition to treating infectious diseases, antibiotics made many modern medical procedures possible, including cancer treatment, organ transplants and open-heart surgery.
[42] Advances in drug discovery based on network pharmacology and omics ... — Advances in drug discovery based on network pharmacology and omics technology - ScienceDirect Advances in drug discovery based on network pharmacology and omics technology This review provides an in-depth introduction to omics technologies, including metabolomics, proteomics, and genomics, and discusses the application of network pharmacology and these omics technologies in drug research. We focuse on the steps of studying cancer treatment drugs using network pharmacology combined with metabolomics, proteomics, and genomics technologies. These advancements have laid a solid foundation for the future development of network pharmacology and omics technologies and summarize the drug research methods based on network pharmacology, providing important references for future drug development. All content on this site: Copyright © 2024 Elsevier B.V., its licensors, and contributors. For all open access content, the Creative Commons licensing terms apply.
[43] Advancements in contemporary pharmacological innovation: Mechanistic ... — Advancements in contemporary pharmacological innovation: Mechanistic insights and emerging trends in drug discovery and development - ScienceDirect Advancements in contemporary pharmacological innovation: Mechanistic insights and emerging trends in drug discovery and development Open access However, recent advancements in various technologies, such as multi-omics, genome editing, Artificial Intelligence (AI), and Machine Learning (ML), have significantly improved this process. These technologies have made the process more accurate, less time-consuming, and cost-effective compared to the conventional methods of drug discovery and development. The review begins by briefly explaining the conventional drug discovery and development process, and then delves into the applications of multi-omics, genome editing technology, systems biology, artificial intelligence, and machine learning. For all open access content, the relevant licensing terms apply.
[44] Advances in Integrated Multi-omics Analysis for Drug-Target ... — Multi-omics is a novel approach and technology for systematically studying biology, integrating and analyzing data from multiple omics levels such as genomics, transcriptomics, proteomics, and metabolomics in an unbiased manner. Examples include mixOmics, which provides a series of statistical methods for exploring and integrating multi-omics datasets, including traditional and regularized multivariate approaches; MetaboAnalyst, a comprehensive web-based platform for classifying, diagnosing, and integrating metabolomic and transcriptomic data; Omics Integrator, which is used to integrate data from different omics studies and identify significant molecular networks; and path view, which maps genomic data onto biological pathway diagrams and cooperates with multi-omics data analysis.
[48] Antibiotics: past, present and future - PubMed — In just over 100 years antibiotics have drastically changed modern medicine and extended the average human lifespan by 23 years. Since then, a gradual decline in antibiotic discovery and development and the evolution of drug resistance in many human pathogens has led to the current antimicrobial resistance crisis. Here we give an overview of the history of antibiotic discovery, the major classes of antibiotics and where they come from. We argue that the future of antibiotic discovery looks bright as new technologies such as genome mining and editing are deployed to discover new natural products with diverse bioactivities.
[49] The role of serendipity in drug discovery - PubMed — "Serendipity" in drug discovery implies the finding of one thing while looking for something else. This was the case in six of the twelve serendipitous discoveries reviewed in this paper, i.e., aniline purple, penicillin, lysergic acid diethylamide, meprobamate, chlorpromazine, and imipramine. ... History, 19th Century History, 20th Century
[85] The future of pharmaceuticals: Artificial intelligence in drug ... — The future of pharmaceuticals: Artificial intelligence in drug discovery and development - ScienceDirect The future of pharmaceuticals: Artificial intelligence in drug discovery and development The applications of AI have been summarized in drug discovery Artificial Intelligence (AI) is revolutionizing traditional drug discovery and development models by seamlessly integrating data, computational power, and algorithms. Coupled with machine learning (ML) and deep learning (DL), AI has demonstrated significant advancements across various domains, including drug characterization, target discovery and validation, small molecule drug design, and the acceleration of clinical trials. However, AI's application in drug development faces challenges, including the need for robust data-sharing mechanisms and the establishment of more comprehensive intellectual property protections for algorithms. For all open access content, the relevant licensing terms apply.
[86] Integrating artificial intelligence in drug discovery and early drug ... — There are several limitations, specific to drug discovery and development in cancer, that can be summarized in the following concepts: (1) High Costs and Long Timelines: 10–15 years for a drug candidate to receive regulatory approval ; (2) Low Success Rates: approximately 90% of candidates that enter early clinical trials do not reach the market ; and (3) Complex Disease Biology: cancer involves complex, interconnected biological pathways that are difficult to target effectively with classical methods. As the main reasons for failures in drug development are insufficient efficacy and safety levels, methods based on AI could help mitigate challenges in the analysis of multiomics data by improving target identification and predicting druggability, which enhances the overall drug discovery process. An example of the integration of biological data for drug identification is PaccMann, an AI-driven framework designed to predict cancer cell sensitivity to compounds by integrating molecular structures, gene expression profiles, and protein interaction data.
[87] The Role of AI in Drug Discovery: Challenges, Opportunities, and ... — Artificial intelligence (AI) has the potential to revolutionize the drug discovery process, offering improved efficiency, accuracy, and speed. AI-based approaches, on the other hand, have the ability to improve the efficiency and accuracy of drug discovery processes and can lead to the development of more effective medications. By combining the predictive power of AI with the expertise and experience of human researchers , it is possible to optimize the drug discovery process and accelerate the development of new medications . Recent developments in AI, including the use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods, offer promising strategies for overcoming the challenges and limitations of AI in the context of drug discovery.
[88] The Rise of Artificial Intelligence in Drug Discovery — The application of Artificial Intelligence (AI) in drug discovery is rapidly transforming the pharmaceutical industry, offering opportunities to accelerate the identification of novel therapeutic targets, optimize molecule design, and enhance clinical trial efficiency. AI's impact on drug discovery is most pronounced in the initial stages: identifying potential drug targets and designing therapeutic molecules. The application of AI in clinical trials has the potential to drastically improve the efficiency of drug development by streamlining the recruitment process and personalizing treatment regimens for individual patients. AI-driven drug discovery relies heavily on large datasets, including sensitive personal health information, such as electronic health records and genomic data. AI is undeniably transforming the drug discovery landscape by enabling faster identification of drug targets, streamlining clinical trials, and personalizing treatment regimens.
[115] The Rise of Patient-Centric Regulatory Approaches: Balancing Innovation ... — Increased patient advocacy and empowerment; Advancements in technology enabling better patient engagement; Recognition of the importance of patient-reported outcomes; Regulatory agencies' emphasis on patient-focused drug development; The FDA's Patient-Focused Drug Development (PFDD) initiative is a prime example of this shift, aiming to
[117] Innovation in Development, Regulatory Review, and Use of Clinical Advances — Eight recommendations in the President's Council of Advisors on Science and Technology 2012 report sought to “double the output of innovative new medicines for patients with important unmet medical needs, while increasing drug efficacy and safety, through industry, academia and government working together to decrease clinical failure, clinical trial costs, time to market and regulatory uncertainty” (PCAST, 2012). They involve the use of RWE and collaborations among sectors of the health care system that will generate knowledge about the best use of drugs, devices, and clinical models of care; cognitive computing to understand the most effective and appropriate interventions for enhanced clinical outcomes; specialists working in their professional organizations to guide clinical care, reduce the current variation in care, and promote evidence-based care; harmonized quality measures and payment instruments; effective leveraging of new organizational structures and their clinical leaders; and the enabling of patients to facilitate shared information and become partners in care.
[118] The applications and advances of artificial intelligence in drug ... — The applications and advances of artificial intelligence in drug regulation: A global perspective - ScienceDirect The applications and advances of artificial intelligence in drug regulation: A global perspective This review aims to provide a comprehensive overview of the current state of AI in drug regulation, encapsulating AI-related policies, initiatives, and its practical application in regulatory agencies globally. Moreover, AI's deployment in safety surveillance, workflow optimization, and regulatory science research is expanding, highlighting its increasing impact on drug regulation. The review concludes that interdisciplinary collaboration is crucial to harness AI's full potential in drug regulation and overcoming its current limitations. This review offers a detailed analysis of the current state of AI in drug regulation. For all open access content, the relevant licensing terms apply.
[121] Frontiers | Drug discovery and development: introduction to the general ... — Finding new drugs usually consists of five main stages: 1) a pre-discovery stage in which basic research is performed to try to understand the mechanisms leading to diseases and propose possible targets (e.g., proteins); 2) the drug discovery stage, during which scientists search for molecules (two main large families, small molecules and biologics) or other therapeutic strategies that interfere or cure the investigated disease or at least alleviate the symptoms; 3) the preclinical development stage that focuses on clarifying the mode of action of the drug candidates, investigates potential toxicity, validates efficacy on various in vitro and in vivo models, and starts evaluate formulation; 4) the clinical stage that investigates the drug candidate in humans; 5) the reviewing, approval and post-market monitoring stage during which the drug is approved or not.
[122] The 5 Drug Development Phases - Phase 1, Phase 2, Phase 3 Clinical ... — The 5 Drug Development Phases - Phase 1, Phase 2, Phase 3 Clinical Trials - DMD Warrior A new medication must pass through five distinct stages in order to be considered a “success”: 1) Discovery and Development; 2) Preclinical Research; 3) Clinical Trials; 4) FDA Drug Review; and 5) FDA Post-Market Safety Monitoring. Before a drug even enters the clinical trial phase, there is a significant amount of preliminary research that occurs. The main goal of Phase 1 trials is to assess the drug’s safety profile, determine a safe dosage range, and identify any side effects. Phase 3 trials are large-scale studies designed to confirm the drug’s efficacy, monitor its side effects, and compare it to existing treatments or a placebo.
[128] Investigating the Food and Drug Administration Biotherapeutics Review ... — Abstract Background The development, review, and approval process of therapeutic biological products in the United States presents two primary challenges: time and cost. Advancing a biotherapeutic from concept to market may take an average of 12 years, with costs exceeding US $1 billion, and the product may still fail the US Food and Drug Administration (FDA) approval process. Despite the FDA
[129] FDA regulatory considerations for oncology drug development — Over the past 20 years, more than 150 oncology drugs have been approved by the US Food and Drug Administration (FDA) including 42 novel therapeutics in 2021-2023 (Table 1). In this highly competitive environment, a proper understanding of the addressable challenges is essential for successful drug development.
[130] Top 5 Challenges in Formulation and Process Development for Drug ... — This article highlights the top 5 challenges in formulation and process development, offering data-driven solutions to streamline drug development and meet regulatory requirements.
[131] Common Challenges in Obtaining FDA Approval for New Drugs — 1. Stringent Regulatory Requirements. The FDA is responsible for regulating the safety and efficacy of drugs in the United States. As a result, the agency has established strict guidelines and regulations that pharmaceutical companies must adhere to in order to obtain approval for their new drugs.
[133] Breaking Barriers: Strategies for Achieving Diversity and Inclusivity ... — Despite the clear benefits of diverse representation in clinical trials, significant barriers prevent the inclusion of underrepresented populations. Understanding these challenges is critical to developing strategies that will improve recruitment and retention of diverse participants.
[136] HHS Actions to Enhance Diversity in Clinical Research — The NIH’s Trial Innovation Network (TIN) develops tools and provides clinical trial support that improves trial efficiency including recruitment of diverse populations into trials by increasing decentralized trial methods and providing methods to address diverse participant needs in addition to addressing system and trial barriers to recruitment.44,45 The TIN developed the on-line program Faster Together, Enhancing the Recruitment of Marginalized Communities Clinical Trials for research teams to identify and develop solutions together to address the barriers and facilitators their study team or sites may have to minority recruitment.46 NIH also supports community health centers through its Community Partnerships to Advance Science (ComPASS) program,47 which seeks to increase diversity and inclusion in research by cultivating community trust and partnerships, building research capacity among the community and relevant partners, and enhancing community organization competitiveness for future funding.
[137] Controlling Bias in Randomized Clinical Trials — The two major factors credited for the lower risk of bias in clinical trials are the use of random treatment assignment for subjects and masking of the assigned treatment. No matter how it is performed, treatment randomization "levels the playing field" so that the treatment groups are typically similar in terms of baseline patient
[138] Identifying and Avoiding Bias in Research - PMC — Sources of pre-trial bias include errors in study design and in patient recruitment. Recall bias is most likely when exposure and disease status are both known at time of study, and can also be problematic when patient interviews (or subjective assessments) are used as a primary data sources. A study's internal validity reflects the author's and reviewer's confidence that study design, implementation, and data analysis have minimized or eliminated bias and that the findings are representative of the true association between exposure and outcome. An ideal trial design would randomize patients and blind those collecting and analyzing data (high internal validity), while keeping exclusion criteria to a minimum, thus making study and source populations closely related and allowing generalization of results (high external validity) 34.
[166] Drug Repurposing: History, Significance, Benefits, Approaches, and ... — Benefits: Drug repurposing leads to faster regulatory approvals due to the availability of existing drugs' preclinical, safety, and tolerability data. Drug repurposing becomes cost-effective since much of the phase I/II and preclinical work has already been completed. Repurposing a medicine is expected to cost an average of $300 million instead of investing $2 to $3 billion in a new chemical
[169] Intellectual Property Rights and Regulatory Considerations for Drug ... — Challenges in Drug Repurposing. Despite its advantages, drug repurposing is not without its challenges. One of the primary hurdles is navigating the complex landscape of intellectual property rights and regulatory requirements. ... FDA Regulations for Repurposed Drugs. In the United States, the Food and Drug Administration (FDA) has established
[171] Drug Repurposing: Legal and Regulatory Issues in the US — While repurposed drugs may not qualify for patents, about half of 505(b)(2) drugs are associated with patents or other statutory exclusivities. ... Drug Repurposing: Legal and Regulatory Issues in the US. Published. proceedings-article. Author(s): Aaron S. Kesselheim. Publication date (Electronic): 30 August 2022 . Conference name: RExPO22
[172] Giving Drugs a Second Chance: Overcoming Regulatory and Financial ... — Regulatory challenges in drug repurposing include demonstrating efficacy for new indications, navigating intellectual property issues, and understanding complex regulatory pathways. Financial hurdles involve securing funding for research, balancing risk and reward for pharmaceutical companies, and addressing pricing and reimbursement challenges.
[173] Overcoming the legal and regulatory barriers to drug repurposing — Abstract Drug repurposing has been proposed as a strategy to develop new therapies that has fewer risks, lower costs and shorter timelines than developing completely new drugs. However, the potential of this strategy has not been as widely realized as hoped, in part owing to legal and regulatory barriers. Here, we highlight these barriers and consider how they could be overcome.
[196] Drug repurposing of fluoroquinolones as anticancer agents in 2023 — Drug repurposing of fluoroquinolones as anticancer agents in 2023. ... exerting their activity via different mechanisms such as induction of cell cycle arrest at different phases or induction of apoptosis through targeting the cancer suppressor gene P53, ... The promising biological results of CP derivatives , , and ,
[197] Drug repurposing screening and mechanism analysis based on human ... — 22 May 2023. Published: 22 June 2023. Corrected and typeset: ... Drug repurposing for cancer therapy is a promising strategy for drug discovery. ... we described the biological mechanisms of drugs and classified the drug response patterns of CRC organoids into five prevalent and representative groups, which represent the general responding
[198] Drug Repurposing: An Effective Tool in Modern Drug Discovery — | Targeted mechanism-based methods | Drug omics data, disease pathway and protein interaction network | Knowledge-based methods require knowledge about drugs or diseases, such as adverse effects, regulatory approval labels, records of clinical trials, and published disease biomarkers (potential targets) or disease pathways). Signature-based drug-repurposing methods use gene signatures derived from disease omics data with or without treatment to discover unknown off-target or disease mechanisms. This method utilizes genetic disease data, available signalling or metabolic pathways, and protein interaction networks to reconstruct disease-specific pathways, thereby identifying the key target for repurposing drugs. 6.Rudrapal, M., Khairnar, S.J., and Jadhav, A.G., Drug Repurposing (DR): An Emerging Approach in Drug Discovery, 2020.
[199] Pharmacological updates of nifuroxazide: Promising preclinical effects ... — Moreover, it has promising effects against sepsis-induced organ injury, hepatic disorders, diabetic nephropathy, ulcerative colitis, and immune disorders. These promising effects appear to be mediated by suppressing STAT3 as well as NF-κB, TLR4, and β-catenin expressions and effectively decreasing downstream cytokines TNF-α, IL-1β, and IL-6.
[207] Modern Approaches in the Discovery and Development of Plant-Based ... — Natural products represents an important source of new lead compounds in drug discovery research. Several drugs currently used as therapeutic agents have been developed from natural sources; plant sources are specifically important. In the past few
[209] The Importance of Natural Products in Drug Discovery ... - Springer — The journey from discovering a natural product (NP)-based drug to its market launch involves multiple stages: initial discovery, drug development, and clinical trials, Showing schematic diagram in Fig. 1 (A&B).This entire process can span around 10 years and cost over $800 million, primarily due to the extensive number of leads discarded along the way (Dickson and Gagnon 2004).
[210] Natural products in drug discovery: advances and opportunities — Advertisement View all journals Search Log in Explore content About the journal Publish with us Subscribe Sign up for alerts RSS feed nature nature reviews drug discovery review articles article Review Article Published: 28 January 2021 Natural products in drug discovery: advances and opportunities Atanas G. Atanasov ORCID: orcid.org/0000-0003-2545-09671,2,3,4, Sergey B. Zotchev2, Verena M. Dirsch ORCID: orcid.org/0000-0002-9261-52932, the International Natural Product Sciences Taskforce & … Claudiu T. Supuran ORCID: orcid.org/0000-0003-4262-03235 Show authorsNature Reviews Drug Discovery volume 20, pages 200–216 (2021)Cite this article 373k Accesses 2791 Citations 715 Altmetric Metrics details Subjects Chemical biology Transferases Abstract Natural products and their structural analogues have historically made a major contribution to pharmacotherapy, especially for cancer and infectious diseases. Nevertheless, natural products also present challenges for drug discovery, such as technical barriers to screening, isolation, characterization and optimization, which contributed to a decline in their pursuit by the pharmaceutical industry from the 1990s onwards. In recent years, several technological and scientific developments — including improved analytical tools, genome mining and engineering strategies, and microbial culturing advances — are addressing such challenges and opening up new opportunities. Here, we summarize recent technological developments that are enabling natural product-based drug discovery, highlight selected applications and discuss key opportunities.
[211] Medicinal Chemistry Strategies for the Modification of Bioactive ... — These structural modifications were implemented to enhance the selectivity of siponimod in its interaction with specific receptors, thereby potentially influencing its pharmacological activity . In initial structure-activity studies, the trifluoromethyl group on the benzene ring was found to significantly impact activity, boosting it by over 30
[212] PDF — in overcoming these challenges by modifying bioactive natural products to enhance their pharmacological properties. This article provides an overview of various medicinal chemistry approaches employed for the modification of bioactive natural products, including structural optimization, semi-synthesis, prodrug design and molecular hybridization.
[213] The structural modification of natural products for novel drug discovery — This article also reveals that modification of NPs is a versatile approach to explore their mode of actions, which may lead to the discovery of novel drugs. Expert opinion: NPs are usually described by structural diversity and complexity. The use of isolated NPs as scaffolds for modification is a good approach to drug discovery and development.
[214] Natural Products in Modern Drug Discovery: Challenges, Advances, And ... — AspectNatural ProductsSynthetic CompoundsSourceDerived from plants, animals, fungi, and microorganisms.Created through chemical synthesis in laboratories.Chemical DiversityHigh diversity with complex structures.Can be designed for specific properties, but often less diverse.Biological ActivityOften exhibit potent bioactivity due to evolutionary adaptation.Activity depends on design; may require extensive optimization.Mechanisms of ActionUnique mechanisms that can target multiple pathways.Mechanisms can be tailored but may lack novelty.Historical SuccessMany approved drugs (e.g., antibiotics, anticancer agents) are derived from natural products.Numerous successful drugs, but often require extensive testing and optimization.Isolation and CharacterizationComplex processes; may require extensive purification.Generally more straightforward; can be synthesized in pure form.SustainabilityConcerns about overexploitation and biodiversity loss.Generally more sustainable as they can be produced in large quantities without depleting natural resources.Regulatory ChallengesComplex regulatory landscape regarding sourcing and intellectual property.More established regulatory pathways for synthetic drugs.VariabilityComposition can vary based on environmental factors and extraction methods.Consistent composition and quality can be achieved through controlled synthesis.Cost of DevelopmentCan be high due to extraction, purification, and variability.Can be lower for well-established synthetic pathways, but initial research and development can be costly.Targeted DeliveryNatural products can be integrated into advanced delivery systems (e.g., nanoparticles).Synthetic compounds can also be designed for targeted delivery, often with more control over formulation.
[215] Study design of herbal medicine clinical trials: a descriptive analysis ... — Clinical trials are considered the gold standard for assessing the efficacy and safety of therapeutics, and as such, an increasing number of studies have been conducted to evaluate the scientific evidence of herbal medicinal products . However, there are distinct characteristics of herbal medicine clinical trials that differ from those of
[216] Clinical Efficacy Trials With Natural Products and Herbal Medicines — Translation of natural and herbal medicines from bench-to-bedside can be a very arduous path. Past trials of preventive agents offer important lessons that can form the basis of design and conduct of future clinical trials such as the need for more preclinical and early phase work before undertaking phase III trials.
[217] The Traditional Medicine and Modern Medicine from Natural Products — When used to develop new drugs, natural products and traditional medicines have their incomparable advantages, such as abundant clinical experiences, and their unique diversity of chemical structures and biological activities. 1.Shi Q.W., Li L.G., Huo C.H., Zhang M.L., Wang Y.F. Study on natural medicinal chemistry and new drug development. 8.Tu P.F., Guo H.Z., Guo D.A. Researches on active constituents of natural and traditional medicine and development of new drugs. 13.Ngo L.T., Okogun J.I., Folk W.R. 21st Century natural product research and drug development and traditional medicines. 28.Zhang A.H., Sun H., Qiu S., Wang X.J. Advancing drug discovery and development from active constituents of yinchenhao tang, a famous traditional Chinese medicine formula.
[222] Genome Mining as New Challenge in Natural Products Discovery — Specifically, the genome mining involves the identification of previously uncharacterized natural product biosynthetic gene clusters within the genomes of sequenced organisms, sequence analysis of the enzymes encoded by these gene clusters, together with the experimental identification of the products of the gene clusters (Figure 1; ). In fact, although the genome mining allowed to find and identify the gene clusters responsible for the production of natural product synthesis, in the last decade web tools and databases have been integrated to improve the performance of this approach . applied genome mining to identify strains capable of producing halogenase enzymes, where halogenations represent an important feature for the biological activity of a great number of different natural products.
[223] Using natural products for drug discovery: the impact of the genomics ... — In the pre-genomic era, most natural product discovery efforts employed a 'top-down' approach driven by the screening biological samples for desirable bioactivities, followed by compound isolation and characterization . Yet by the 1990s, such strategies had largely failed to uncover new natural products as pharmaceutical companies struggled
[224] Applications and prospects of genome mining in the discovery of natural ... — With the advent of the genomic era, the computational mining of genomes has become an important part in the discovery of novel natural products as drug leads. Meanwhile, the development of high-throughput sequencing and the establishment of DNA database, genome mining methods and tools have contributed to the discovery and characterization of
[225] Genome Mining as New Challenge in Natural Products Discovery — Furthermore, in the genomic era, in which the number of available genomes is increasing, genome mining joined to synthetic biology are offering a significant help in drug discovery. In the present review we discuss the importance of genome mining and synthetic biology approaches to identify new natural products, also underlining considering the
[226] Self-resistance-gene-guided, high-throughput automated genome mining of ... — Yuan et al. present a fully automated, high-throughput platform for discovering natural product biosynthetic gene clusters in Streptomyces species, offering immense potential to accelerate the discovery and development of pharmaceutical agents for diverse therapeutic applications.
[231] AI-driven drug discovery from natural products - ScienceDirect — Artificial intelligence plays a key role in the exploration of natural product drugs, widely used in the research & development of medicine and pesticide, it accelerates various critical stages from target discovery, hit identification, lead optimization, drug-likeness analysis, pesticide-likeness prediction, physicochemical and toxicological
[232] Artificial intelligence for natural product drug discovery — Artificial intelligence for natural product drug discovery | Nature Reviews Drug Discovery Skip to main content Thank you for visiting nature.com. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.
[234] Pharmaceutical Technology for Improving the Bioavailability of Natural ... — 1.4.1 Particle Size Reduction. Particle size reduction is one of the old and widely used techniques for increase in solubility and bioavailability of drug substance. Reduction in particle size offers most promising technique to improve the bioavailability of hydrophobic drugs because it increases surface area, increase wettability of drug and enhanced saturation solubility.
[236] Bioavailability Enhancement Techniques | Arborpharmchem — Cyclodextrins can improve absorption and help design more effective medicinal products due to their versatility and safety. Prodrug Plans. Chemically modifying APIs into prodrugs improves bioavailability. Modifying the API often involves adding a promoiety. Enhancing solubility, permeability, and metabolic stability can increase bioavailability.
[237] Enhancing bioavailability of natural extracts for nutritional ... — Studies have shown that spray drying can significantly improve the efficiency of drug absorption in the lung, improve drug distribution, avoid drug deposition, increase its bioavailability, and allow natural products to exert their target clinical effects (136, 137). For example, in amorphous formulations obtained by spray drying ferulic acid
[239] Pharmaceutical Technology for Improving the Bioavailability of Natural ... — Since last one decade novel drug delivery system emerged as successful method to improve bioavailability of natural product by encapsulating the active pharmaceutical ingredients into the nano-cargo which includes phytosome, liposomes, nanoparticles, nanoemulsion, transferosomes, ethosomes, lipid based systems, microspheres (Kesarwani et al
[243] Common challenges and solutions in drug discovery process — Drug discovery is a complex and challenging process that involves the comprehensive application of multiple links and disciplinary knowledge. The following are common challenges and solutions in the process of drug discovery: one、 Common challenges 1. Scientific challenge: Drug discovery requires a deep understanding of the biological mechanisms of diseases and finding effective therapeutic
[244] 5 Drug Discovery and Development Challenges and How to Solve Them - SRG — Importance of drug discovery and development. Drug discovery and development is a complex, and ever-evolving process that can take between 8-12 years and cost on average, over £1 billion.On top of this already astronomical amount, regulatory pressures on new drug approvals, and payor influence on drug pricing add extra costs that make establishing a reliable revenue increasingly challenging
[246] New challenges in drug discovery - ScienceDirect — Drug discovery is a process by which novel potential medicines are identified via a series of critical research activities (Sang et al., 2018).It comprises a variety of scientific fields, including pharmacology, medicinal chemistry, biochemistry, and biology (Rufer, 2021).The drug discovery centers and industries have supplied several life-saving medications for decades, which contribute to
[247] Modern Challenges of Drug Discovery - AZoLifeSciences — New technology has been a key driver of such advances, with breakthroughs in assay technology, automation, imaging, nanofluidics, and software helping to significantly improve the process of drug discovery. In short, these are the length, complexity, uncertainty, and cost of the process; the unknown pathophysiology of many disorders, making target identification difficult; the limitations of animal models; the heterogeneity of the patient population making one-size-fits-all drugs limited in success; and the lack of validated diagnostic and therapeutic biomarkers. However, much more research and development is needed before this challenge to modern drug discovery can be overcome. Retrieved on November 18, 2024 from https://www.azolifesciences.com/article/Modern-Challenges-of-Drug-Discovery.aspx. <https://www.azolifesciences.com/article/Modern-Challenges-of-Drug-Discovery.aspx>. https://www.azolifesciences.com/article/Modern-Challenges-of-Drug-Discovery.aspx. AZoLifeSciences, viewed 18 November 2024, https://www.azolifesciences.com/article/Modern-Challenges-of-Drug-Discovery.aspx.
[251] Integrating artificial intelligence in drug discovery and early drug ... — There are several limitations, specific to drug discovery and development in cancer, that can be summarized in the following concepts: (1) High Costs and Long Timelines: 10–15 years for a drug candidate to receive regulatory approval ; (2) Low Success Rates: approximately 90% of candidates that enter early clinical trials do not reach the market ; and (3) Complex Disease Biology: cancer involves complex, interconnected biological pathways that are difficult to target effectively with classical methods. As the main reasons for failures in drug development are insufficient efficacy and safety levels, methods based on AI could help mitigate challenges in the analysis of multiomics data by improving target identification and predicting druggability, which enhances the overall drug discovery process. An example of the integration of biological data for drug identification is PaccMann, an AI-driven framework designed to predict cancer cell sensitivity to compounds by integrating molecular structures, gene expression profiles, and protein interaction data.
[255] Navigating Regulatory Challenges in Multi-Country Trial Sites — This blog post will discuss the specific challenges of conducting multi-country clinical trials and provide actionable strategies to ensure compliance, optimize timelines, and streamline the approval process. Each country involved in the trial has its own regulatory authority, with specific requirements for approval, data collection, safety reporting, and ethical review. Ensuring compliance with patient safety and data privacy laws is critical in multi-country clinical trials. To navigate the complexities of multi-country clinical trials effectively, sponsors and CROs must adopt strategic approaches to ensure regulatory compliance, harmonize protocols, and streamline the approval process. Successfully navigating the regulatory landscape of multi-country clinical trials requires careful planning, early engagement with regulatory authorities, and a deep understanding of local regulations and ethical standards.
[261] Top 5 Challenges in Formulation and Process Development for Drug ... — Quality by Design (QbD): QbD is a systematic approach to drug development that ensures product quality by focusing on the design of the formulation and manufacturing process. According to American Pharmaceutical Review, companies that enforced strict raw material specifications and in-process quality testing experienced a 15% reduction in batch failures, leading to more reliable product quality . At ARSI Canada, we specialize in helping pharmaceutical companies overcome these challenges with our comprehensive formulation development and process development consulting services. With ARSI Canada’s expertise, companies can streamline their drug development processes, reduce time to market, and improve product quality. By addressing issues like solubility, scalability, regulatory compliance, and raw material variability through innovative strategies, companies can reduce costs, improve efficiency, and bring drugs to market faster.
[264] The economic challenges of new drug development - ScienceDirect — In this perspective, we reflect on some key economic obstacles in drug development. We argue that the world's collective experience of the pandemic may present an opportunity to reform traditional economic models of drug discovery to help address present and future unmet needs.
[266] Drug discovery: new models for industry-academic partnerships — Innovative funding programmes such as the Wellcome Trust's £91 million Seeding Drug Discovery initiative, ... The Imperial drug discovery model aims to be a true partnership between industry and academia throughout the drug discovery process, utilising the skills and capabilities of each partner rather than reproducing scarce, unique or
[267] Breaking the Bank: Three Financing Models for Addressing the Drug ... — We describe 3 innovative financing models to manage expensive specialty drugs that will significantly reduce the direct, immediate cost burden of these drugs to public and private healthcare payers. The 3 financing models include high-cost drug mortgages, high-cost drugs reinsurance, and high-cost drug patient rebates.
[282] Unleashing the future: The revolutionary role of machine learning and ... — These advanced technologies have revamped traditional drug discovery methods by allowing quick analysis of complex biological data and finding new therapeutic targets (Paul et al., 2021; Patel and Shah, 2022). Additionally, integrating AI and ML with advanced imaging technologies like high-throughput microscopy and single-cell sequencing provides new avenues for discovering and validating drug targets (Singh et al., 2023). By merging imaging data with AI and deep learning algorithms, researchers can uncover fresh drug targets and gain deeper insights into disease mechanisms, developing more potent therapies (Pun et al., 2023). By examining molecular and clinical data from various origins, including electronic health records and preclinical investigations, AI algorithms can unveil previously unknown correlations between drugs and biological targets while also predicting potential side effects and drug-drug interactions (Qureshi et al., 2023; Yang and Kar, 2023).
[283] The future of pharmaceuticals: Artificial intelligence in drug ... — The future of pharmaceuticals: Artificial intelligence in drug discovery and development - ScienceDirect The future of pharmaceuticals: Artificial intelligence in drug discovery and development The applications of AI have been summarized in drug discovery Artificial Intelligence (AI) is revolutionizing traditional drug discovery and development models by seamlessly integrating data, computational power, and algorithms. Coupled with machine learning (ML) and deep learning (DL), AI has demonstrated significant advancements across various domains, including drug characterization, target discovery and validation, small molecule drug design, and the acceleration of clinical trials. However, AI's application in drug development faces challenges, including the need for robust data-sharing mechanisms and the establishment of more comprehensive intellectual property protections for algorithms. For all open access content, the relevant licensing terms apply.
[285] The recent advances in the approach of artificial intelligence (AI ... — Through the synergistic combination of AI’s predictive capabilities and the insights derived from the expertise and experience of human researchers, there exists an opportunity to optimize the drug discovery process and expedite the development of new medications (Hasselgren and Oprea, 2024; Alharbi et al., 2024; Zhang et al., 2024; Shi et al., 2022; Kang et al., 2020; Bibbò et al., 2017; Khan et al., 2020a; Iqbal et al., 2019; Khan et al., 2018a; Khan et al., 2021a; Jamil et al., 2021; Khan et al., 2020b; Tareen et al., 2021a; Khan et al., 2023; Khan et al., 2020c; Khan et al., 2021b; Tareen et al., 2022a; Khan et al., 2019a; Cao et al., 2012; Zhang et al., 2019; Hu et al., 2020; Tareen et al., 2022b; Khan et al., 2019b; Khan et al., 2021c; Khan et al., 2021d; Khan et al., 2021e; Aslam et al., 2021; Ahmad et al., 2021a; Shaheen et al., 2023; Li et al., 2023; Tang et al., 2021; Khan et al., 2019c; Khan et al., 2019d; Khatoon et al., 2020; Khan et al., 2018b; Khan et al., 2020d; Khan et al., 2018c; Khan et al., 2018d; Ahmad et al., 2021b; Duan et al., 2023; Dai et al., 2018) (Figure 2A).
[286] Artificial Intelligence Applied to clinical trials: opportunities and ... — Main opportunities discussed aim to create efficiencies across CT activities, including the ability to reduce sample sizes, improve enrollment and conduct faster, more optimized adaptive CTs. While AI is an area of enthusiastic development, the identified challenges are ethical in nature and relate to data availability, standards, and most importantly, lack of regulatory guidance hindering the acceptance of AI tools in drug development. Pre-clinical research Early use of AI in pre-clinical research, impacting subsequent CTs. Design Use of AI enabling prediction of outcomes and disease progression to shape or improve Design of CTs. Recruitment Use of AI in Recruitment, which includes Enrollment, defined as the identification of eligible participants and onboarding them into suitable CTs. Conduct Use of AI in Conduct refers to the period following a participant’s enrollment into the trial, up to the trial database lock, prior to statistical analysis.
[288] Ethical Considerations in the Use of Artificial Intelligence and ... — By addressing privacy and data security concerns proactively and transparently, healthcare organizations can build trust with patients, mitigate ethical risks associated with AI and ML applications, and harness the full potential of these technologies to improve patient care and advance medical research, while safeguarding patient privacy and autonomy. By ensuring that healthcare providers and patients understand the rationale behind algorithmic recommendations and the limitations of AI-driven decision-making, healthcare organizations can promote the ethical and responsible use of AI and ML in health care, ultimately improving patient outcomes and advancing the delivery of personalized, evidence-based care . By addressing issues such as data privacy and security, algorithmic bias, transparency, clinical validation, and professional responsibility, healthcare stakeholders can navigate the ethical complexities surrounding AI and ML integration in health care, while safeguarding patient welfare and upholding the principles of beneficence, non-maleficence, autonomy, and justice.
[290] The Role of AI in Drug Discovery - Chemistry Europe — The Role of AI in Drug Discovery M. K. G. Abbas,*[a] Abrar Rassam,[b] ... drug repurposing, and the prediction of drug proper-ties such as toxicity, bioactivity, and physicochemical character-istics. Despite AI's promising advancements, the paper also ... facilitating early predictions of drug efficacy and safety. Furthermore, AI