Enhancing the Hit-to-Lead Properties of Lead Optimization Libraries

TLDR

Multipharmacophore descriptors were developed to design diverse compound libraries, leveraging the pharmacophore hypothesis to understand compound–protein interactions and to sample all potential pharmacophores for biologically relevant diversity. The paper aims to improve lead optimization libraries by addressing design issues, developing simple descriptors for drug transport properties, and presenting a Monte Carlo search for near‑combinatorial subsets meeting design criteria. The authors derive simple, rapidly computable descriptors for drug transport properties and apply them in library design, using a Monte Carlo search to select near‑combinatorial subsets that satisfy design criteria, sometimes requiring noncombinatorial synthesis. The study shows that multipharmacophore descriptors can be portably applied to diverse library designs, including focused libraries, and that incorporating calculated log P, molecular weight, and polar surface area into combinatorial library design yields compounds with improved absorption as measured by Caco‑2 assays.

Abstract

In this paper we address several issues in the design of lead optimization libraries. Multipharmacophore descriptors were first developed in the context of designing diverse compound libraries. One reason for favoring such descriptors is the importance of the pharmacophore hypothesis in understanding the interaction of a compound with a protein target. Allied to this is the proposal that sampling over all potential pharmacophores leads to diversity in a biologically relevant space. We present results in support of this argument and also demonstrate that such methods are applicable to the design of focused libraries where the aim is to design the library toward a known lead or leads. This portability is important because it means that the same descriptors can be used for diverse library design, screening set selection, and focused library design, giving a consistent approach. We also examine the question of designing libraries with improved pharmacokinetic properties and show that it is possible to derive simple and rapidly computable descriptors applicable to the prediction of drug transport properties. Furthermore, these can be applied in the context of library design, although it may be necessary to synthesize libraries in a noncombinatorial manner to obtain the best results. To address this problem, we describe a Monte Carlo search procedure that allows the selection of a near-combinatorial subset in which all library members satisfy the design criteria. We present an example from our own work that illustrates how consideration of calculated log P, molecular weight, and polar surface area in the design of a combinatorial library can lead to compounds with improved absorption characteristics as determined by experimental Caco-2 measurements.