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Solubility optimization is a crucial step to obtaining oral PROTACs. Here we measured the thermodynamic solubilities (log S) of 21 commercial PROTACs. Next, we measured BRlogD and log k w IAM (lipophilicity), EPSA, and Δ log k w IAM (polarity) and showed that lipophilicity plays a major role in governing log S, but a contribution of polarity cannot be neglected. Two-/three-dimensional descriptors calculated on conformers arising from conformational sampling and steered molecular dynamics failed in modeling solubility. Infographic tools were used to identify a privileged region of soluble PROTACs in a chemical space defined by BRlogD, log k w IAM and topological polar surface area, while machine learning provided a log S classification model. Finally, for three pairs of PROTACs we measured the solubility, lipophilicity, and polarity of the building blocks and identified the limits of estimating PROTAC solubility from the synthetic components. Overall, this paper provides promising guidelines for optimizing PROTAC solubility in early drug discovery programs.

This study describes the design and implementation of a new chromatographic descriptor called log k'80 PLRP-S that provides information about the lipophilicity of drug molecules in the nonpolar environment, both in their neutral and ionized form. The log k'80 PLRP-S obtained on a polymeric column with acetonitrile/water mobile phase is shown to closely relate to log Ptoluene (toluene dielectric constant ε ∼ 2). The main intermolecular interactions governing log k'80 PLRP-S were deconvoluted using the Block Relevance (BR) analysis. The information provided by this descriptor was compared to ElogD and calclog Ptol, and the differences are highlighted. The "charge-flush" concept is introduced to describe the sensitivity of log k'80 PLRP-S to the ionization state of compounds in the pH range 2 to 12. The ability of log k'80 PLRP-S to indicate the propensity of neutral molecules and monoanions to form Intramolecular Hydrogen Bonds (IMHBs) is proven through a number of examples.

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A major issue related to chromatographic determination of lipophilicity is about the conversion to log P. 5The interconversion of lipophilicity indexes can be made only if two systems express the same balance of intermolecular solute/system forces. The deconvolution of intermolecular interactions is generally obtained by solvation parameter models. Block Relevance (BR) analysis is a new tool specifically designed for medicinal chemists to interpret partitioning/retention phenomena in a very practical way. This paper describes the application of BR analysis to literature data (ElogP) and experimentally 10 determined chromatographic indexes on a Supelcosil TM LC-ABZ column for a series of 36 drugs. Results indicate that BR analysis is a solid and reliable tool that captures the main information encoded in any lipophilicity descriptor.

Aim: To set up a chromatographic strategy for the determination of log P for beyond Rule of 5 (bRo5) drugs. Materials & methods: Capacity factors measured by reverse phase-HPLC. Balance of intermolecular interactions governing systems assessed by partial least squares regression (PLSR) coupled with block relevance anaysis (PLSR-BR) and multiblock PLSR (MBPLSR). Determination of virtual log P obtained through conformational sampling. Results: log k′60 is highly correlated with log P for a dataset of 36 Ro5 compliant compounds (R2 = 0.93, Q2 = 0.90). We refer to the value generated via this method as BRlogP. The balance of intermolecular forces controlling BRlogP and log P are very similar. The ElogPs measured for the bRo5 dataset are significantly higher than corresponding BRlogP. Conclusion: The combination of BRlogP and ElogP provides an experimental lipophilicity range for bRo5 compounds.

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Implementation of IMHB considerations in drug discovery needs robust and validated descriptors to experimentally verify the propensity of compounds to exhibit IMHBs. The first part of the paper presents an overview of the most common techniques to measure the propensity of compounds to form IMHBs. Then we review and discuss recently proposed high throughput (HT) physicochemical descriptors ( Δlog , EPSA and log'80 PLRP-S) which provide the same information. Analysis of the available data enabled us to extract guidelines for the application of these descriptors in drug discovery programs.