Use of 2,6-diaminopurine as a potent suppressor of UGA premature stop codons in cystic fibrosis.

Nonsense mutations are responsible for around 10% of cases of genetic diseases, including cystic fibrosis. 2,6-diaminopurine (DAP) has recently been shown to promote efficient readthrough of UGA premature stop codons. In this study, we show that DAP can correct a nonsense mutation in the Cftr gene in vivo in a new CF mouse model, in utero, and through breastfeeding, thanks, notably, to adequate pharmacokinetic properties. DAP turns out to be very stable in plasma and is distributed throughout the body. The ability of DAP to correct various endogenous UGA nonsense mutations in the CFTR gene and to restore its function in mice, in organoids derived from murine or patient cells, and in cells from patients with cystic fibrosis reveals the potential of such readthrough-stimulating molecules in developing a therapeutic approach. The fact that correction by DAP of certain nonsense mutations reaches a clinically relevant level, as judged from previous studies, makes the use of this compound all the more attractive.

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).