Kinetic solubility: Experimental and machine-learning modeling perspectives.

Kinetic aqueous or buffer solubility is important parameter measuring suitability of compounds for high throughput assays in early drug discovery while thermodynamic solubility is reserved for later stages of drug discovery and development. Kinetic solubility is also considered to have low inter-laboratory reproducibility because of its sensitivity to protocol parameters [1]. Presumably, this is why little efforts have been put to build QSPR models for kinetic in comparison to thermodynamic aqueous solubility. Here, we investigate the reproducibility and modelability of kinetic solubility assays. We first analyzed the relationship between kinetic and thermodynamic solubility data, and then examined the consistency of data from different kinetic assays. In this contribution, we report differences between kinetic and thermodynamic solubility data that are consistent with those reported by others [1, 2] and good agreement between data from different kinetic solubility campaigns in contrast to general expectations. The latter is confirmed by achieving high performing QSPR models trained on merged kinetic solubility datasets. The poor performance of QSPR model trained on thermodynamic solubility when applied to kinetic solubility dataset reinforces the conclusion that kinetic and thermodynamic solubilities do not correlate: one cannot be used as an ersatz for the other. This encourages for building predictive models for kinetic solubility. The kinetic solubility QSPR model developed in this study is freely accessible through the Predictor web service of the Laboratory of Chemoinformatics (https://chematlas.chimie.unistra.fr/cgi-bin/predictor2.cgi).

DMSO Solubility Assessment for Fragment-Based Screening.

In this paper, we report comprehensive experimental and chemoinformatics analyses of the solubility of small organic molecules ("fragments") in dimethyl sulfoxide (DMSO) in the context of their ability to be tested in screening experiments. Here, DMSO solubility of 939 fragments has been measured experimentally using an NMR technique. A Support Vector Classification model was built on the obtained data using the ISIDA fragment descriptors. The analysis revealed 34 outliers: experimental issues were retrospectively identified for 28 of them. The updated model performs well in 5-fold cross-validation (balanced accuracy = 0.78). The datasets are available on the Zenodo platform (DOI:10.5281/zenodo.4767511) and the model is available on the website of the Laboratory of Chemoinformatics.

Identification of new lead molecules against anticancer drug target TFIIH subunit P8 using biophysical and molecular docking studies.

The identification of molecules, which could modulate protein-protein interactions (PPIs), is of primary interest to medicinal chemists. Using biophysical methods during the current study, we have screened 76 compounds (grouped into 16 mixtures) against the p8 subunit of the general transcription factor (TFIIH), which has recently been validated as an anti-cancer drug target. 10% of the tested compounds showed interactions with p8 protein in STD-NMR experiments. These results were further validated by molecular docking studies where interactions between compounds and important amino acid residues were identified, including Lys20 in the hydrophobic core of p8, and Asp42 and 43 in the ß3 strand. Moreover, these compounds were able to destabilize the p8 protein by negatively shifting the Tm (≥2 °C) in thermal shift assay. Thus, this study has identified 8 compounds which are likely negative modulators of p8 protein stability, and could be further considered as potential anticancer agents.

Small molecule-based targeting of TTD-A dimerization to control TFIIH transcriptional activity represents a potential strategy for anticancer therapy.

The human transcription factor TFIIH is a large complex composed of 10 subunits that form an intricate network of protein-protein interactions critical for regulating its transcriptional and DNA repair activities. The trichothiodystrophy group A protein (TTD-A or p8) is the smallest TFIIH subunit, shuttling between a free and a TFIIH-bound state. Its dimerization properties allow it to shift from a homodimeric state, in the absence of a functional partner, to a heterodimeric structure, enabling dynamic binding to TFIIH. Recruitment of p8 at TFIIH stabilizes the overall architecture of the complex, whereas p8's absence reduces its cellular steady-state concentration and consequently decreases basal transcription, highlighting that p8 dimerization may be an attractive target for down-regulating transcription in cancer cells. Here, using a combination of molecular dynamics simulations to study p8 conformational stability and a >3000-member library of chemical fragments, we identified small-molecule compounds that bind to the dimerization interface of p8 and provoke its destabilization, as assessed by biophysical studies. Using quantitative imaging of TFIIH in living mouse cells, we found that these molecules reduce the intracellular concentration of TFIIH and its transcriptional activity to levels similar to that observed in individuals with trichothiodystrophy owing to mutated TTD-A Our results provide a proof of concept of fragment-based drug discovery, demonstrating the utility of small molecules for targeting p8 dimerization to modulate the transcriptional machinery, an approach that may help inform further development in anticancer therapies.