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Reactivities of acrylamide warheads toward cysteine targets: a QM/ML approach to covalent inhibitor design
No full textCovalent inhibition offers many advantages over non-covalent inhibition, but covalent warhead reactivity must be carefully balanced to maintain potency while avoiding unwanted side effects. While warhead reactivities are commonly measured with assays, a computational model to predict warhead reactivities could be useful for several aspects of the covalent inhibitor design process. Studies have shown correlations between covalent warhead reactivities and quantum mechanic (QM) properties that describe important aspects of the covalent reaction mechanism. However, the models from these studies are often linear regression equations and can have limitations associated with their usage. Applications of machine learning (ML) models to predict covalent warhead reactivities with QM descriptors are not extensively seen in the literature. This study uses QM descriptors, calculated at different levels of theory, to train ML models to predict reactivities of covalent acrylamide warheads. The QM/ML models are compared with linear regression models built upon the same QM descriptors and with ML models trained on structure-based features like Morgan fingerprints and RDKit descriptors. Cross-validation tests show that the QM/ML models outperform the linear regression models
and the structure-based ML models, and literature test sets demonstrate the power of the QM/ML models to predict reactivities of unseen acrylamide warhead scaffolds. Ultimately, these QM/ML models are effective, computationally feasible tools that can expedite the design of new covalent inhibitors
Evaluation of Thermal Support during Anesthesia Induction on Body Temperature in C57BL/6 and Nude Mice.
No full textHeat supplementation during surgery is a common practice; however, thermal support is not commonly used during anesthesia induction. Mice lose body temperature quickly, and air movement can exacerbate this, potentially putting mice at a thermal deficit before surgery. Whether the method of warming during induction affects overall heat loss during anesthesia is unknown. We hypothesized that the method of heating would affect body temperature (Tb) during anesthesia induction, maintenance, recovery, and once placed back on the rack. Mice (C57BL/6NHsd-6M/6F [C57BL/6]; Hsd:Athymic Nude-Foxn1nu [Nude]; N = 24;12M/12F) were assigned to a treatment in a factorial design: thermal chamber (TC; ambient temperature [Ta] = 28.8 °C); heating pad (HP; induction chamber placed on an electric heating pad;Ta = 28.4 °C); and control (Ctrl; Ta = 21.6 °C). During induction, one mouse at a time was anesthetized with isoflurane over a 3 min period and then maintained under anesthesia for 10 min on a hot water heating pad (33 °C). Then isoflurane was stopped and time to ambulation was recorded. Tb and activity were tracked in the home cage on the rack before and after anesthesia. During induction, Ctrl mice lost significantly more heat (-2.8 °C) than did TC (+0.2 °C) and HP mice (+0.1 °C) but TC and HP were not different. During anesthesia maintenance, Ctrl mice regained 1 °C, but their Tb was still lower than that of the treated groups. Nude mice consistently had a lower Tb than C57BL/6 mice, regardless of treatment or anesthesia phase. C57BL/6 Ctrl mice took longer to ambulate than either HP or TC mice, but the method of heating did not differentially affect Nude mice. In general, C57BL/6 as compared with Nude and females as compared with males were comparatively more active and had higher Tb during certain times of day, regardless of the heating methods. Overall, our findings support the provision of heat during anesthesia induction, regardless of method, to reduce overall Tb loss during a short anesthesia event
Electrophysiological changes in the rabbit ventricular wedge assay and in human-induced pluripotent stem-cell derived (IPSC) cardiomyocytes provided correlates to severe arrhythmia observed in a dog toxicology study, whereas standard in vitro ion channel assays were not predictive of adverse cardiovascular change
No full textDuring the drug discovery process, low molecular weight compounds are typically assayed in vitro for secondary pharmacology or “off-target” effects, which includes ion channels relevant to cardiac physiology. These assays generally preceed testing in animals. Compound A was an irreversible inhibitor of myeloperoxidase investigated for treatment of peripheral artery disease. Oral doses in dogs at ≥5 mg/kg resulted in cardiac arrhythmias in a dose-dependent fashion (at Cmax,free ≥1.53 µM) that progressed in severity with time. Nevertheless, a panel of 13 ion channel (K, Na, Ca) assays, including hERG, did not identify pharmacologic risks of the molecule. Compound A and related Compound B were subsequently evaluated for electrophysiological effects in the isolated rabbit ventricular wedge assay. Compounds A and B prolonged QT and Tp-e intervals at ≥1 and ≥0.3 µM, respectively, and both prolonged QRS at ≥5 µM. Compound A produced early after depolarizations and premature ventricular complexes at ≥5 µM. These data indicate both compounds can inhibit hERG (Ikr) and Nav1.5 ion channels. In human IPSC cardiomyocytes, Compounds A and B prolonged field potential duration at ≥3 µM and induced cellular dysrhythmia at ≥10 and ≥3 µM, respectively, further supporting a pro-arrhythmic liability. In a repeat-dose rat toxicology study, heart tissue:plasma concentration ratios for Compound A were ≥19X at 24 hours post-dose, indicating significant distribution into cardiac tissue. In conclusion, in vitro ion channel assays may not identify cardiovascular risks observed in vivo, which can be affected by tissue drug distribution. Risk for arrhythmia may increase with a “trappable” ion channel inhibitor, particularly if cardiac tissue drug levels achieve a critical threshold for pharmacologic effect
Application of machine learning models for property prediction to targeted protein degraders.
No full textMachine learning (ML) systems can model quantitative structure-property relationships (QSPR) using existing experimental data and make property predictions for new molecules. With the advent of modalities such as targeted protein degraders (TPD), the applicability of QSPR models is questioned and ML usage in TPD-centric projects remains limited. Herein, ML models are developed and evaluated for TPDs' property predictions, including passive permeability, metabolic clearance, cytochrome P450 inhibition, plasma protein binding, and lipophilicity. Interestingly, performance on TPDs is comparable to that of other modalities. Predictions for glues and heterobifunctionals often yield lower and higher errors, respectively. For permeability, CYP3A4 inhibition, and human and rat microsomal clearance, misclassification errors into high and low risk categories are lower than 4% for glues and 15% for heterobifunctionals. For all modalities, misclassification errors range from 0.8% to 8.1%. Investigated transfer learning strategies improve predictions for heterobifunctionals. This is the first comprehensive evaluation of ML for the prediction of absorption, distribution, metabolism, and excretion (ADME) and physicochemical properties of TPD molecules, including heterobifunctional and molecular glue sub-modalities. Taken together, our investigations show that ML-based QSPR models are applicable to TPDs and support ML usage for TPDs' design, to potentially accelerate drug discovery
Database of 4 Million Medicinal Chemistry-Relevant Ring Systems.
No full textCentral ring systems are the most important part of bioactive molecules. They determine molecule shape, keep substituents in their proper positions, and also influence global molecular properties. In the present study, a database of 4 million medicinal chemistry-relevant ring systems has been created, not by crude random enumeration but by applying a set of rules derived by analyzing rings present in bioactive molecules. The aromatic properties and tautomer stability of generated rings have also been considered to ensure that the rings in the database are stable and chemically reasonable. 99.2% of these rings are novel and not included in molecules in the ChEMBL or PubChem databases. This large database of ring systems has been created with the goal to provide support for bioisosteric design and scaffold hopping as well as to be used in generative chemistry applications. The complete set of created rings is available for download in the SMILES format from https://peter-ertl.com/molecular/data/
Corin and Left Atrial Cardiomyopathy, Hypertension, Arrhythmia, and Fibrosis.
No full textNo abstract: Correspondence to Editor
Do you want to stay single? Considerations on single arm trials in the post-regulatory space
No full textno abstrac
Solvent switching in continuous multi-step chemo-enzymatic synthesis: chiral amino pyridine derivatives as a case study
No full textChiral α-(hetero)aryl primary amines are gaining momentum for their biological activities and their use as building blocks in more complex molecules. Here we report the synthesis of a 2-acetyl-6-pyridine derivative through a continuous chemoenzymatic strategy enabled throughout by careful solvent selection and phase switching. Combining a first biocatalytic transamination reaction performed by TsRTA in a biphasic system in continuous flow, with in line Boc-protection and Suzuki coupling of a boronic acid, we achieved the final product in a >99% conversion. This strategy not only constitues an important example of chemoenzymatic combinations in continuous flow, but highlights the importance of the reaction design to minimize waste (through unreacted substrate recirculation), avoid time intensive workups (through inline extractions) and achieve the product in excellent enantiomeric excess (99% e.e. and 68 mg/L·h
Application of machine learning models for property prediction to targeted protein degraders.
No full textMachine learning (ML) systems can model quantitative structure-property relationships (QSPR) using existing experimental data and make property predictions for new molecules. With the advent of modalities such as targeted protein degraders (TPD), the applicability of QSPR models is questioned and ML usage in TPD-centric projects remains limited. Herein, ML models are developed and evaluated for TPDs' property predictions, including passive permeability, metabolic clearance, cytochrome P450 inhibition, plasma protein binding, and lipophilicity. Interestingly, performance on TPDs is comparable to that of other modalities. Predictions for glues and heterobifunctionals often yield lower and higher errors, respectively. For permeability, CYP3A4 inhibition, and human and rat microsomal clearance, misclassification errors into high and low risk categories are lower than 4% for glues and 15% for heterobifunctionals. For all modalities, misclassification errors range from 0.8% to 8.1%. Investigated transfer learning strategies improve predictions for heterobifunctionals. This is the first comprehensive evaluation of ML for the prediction of absorption, distribution, metabolism, and excretion (ADME) and physicochemical properties of TPD molecules, including heterobifunctional and molecular glue sub-modalities. Taken together, our investigations show that ML-based QSPR models are applicable to TPDs and support ML usage for TPDs' design, to potentially accelerate drug discovery
Revolutionizing Health Care Management: The 8Ps Value Proposition
No full textIn the intricate landscape of modern health care, the Genolier Swiss Medical Network introduces the 8Ps Value Proposition, a multifaceted framework aimed at transforming health care management. This article delves into each dimension of the framework, integrating Predictive, Preventive, Personalized, Participative, Purpose, Platform, Performance Sharing, and Pooling components, also through practical case studies. It cultivates a patient-centric, proactive health ecosystem, prioritizing quality of care, cost-efficiency and health maintenance, informed by the Réseau de l’Arc's integrated care model. By harmonizing these principles, the 8Ps framework aligns seamlessly with contemporary management strategies in healthcare