DeepCt: Predicting Pharmacokinetic Concentration-Time Curves and Compartmental Models from Chemical Structure Using Deep Learning.

After initial triaging using in vitro absorption, distribution, metabolism, and excretion (ADME) assays, pharmacokinetic (PK) studies are the first application of promising drug candidates in living mammals. Preclinical PK studies characterize the evolution of the compound's concentration over time, typically in rodents' blood or plasma. From this concentration-time (-) profiles, PK parameters such as total exposure or maximum concentration can be subsequently derived.

Application of machine learning models for property prediction to targeted protein degraders.

Machine 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.

Deep Learning Models Compared to Experimental Variability for the Prediction of CYP3A4 Time-Dependent Inhibition.

Most drugs are mainly metabolized by cytochrome P450 (CYP450), which can lead to drug-drug interactions (DDI). Specifically, time-dependent inhibition (TDI) of CYP3A4 isoenzyme has been associated with clinically relevant DDI. To overcome potential DDI issues, high-throughput assays were established to assess the TDI of CYP3A4 during the discovery and lead optimization phases.

Adapting Deep Learning QSPR Models to Specific Drug Discovery Projects.

Medicinal chemistry and drug design efforts can be assisted by machine learning (ML) models that relate the molecular structure to compound properties. Such quantitative structure-property relationship models are generally trained on large data sets that include diverse chemical series (global models). In the pharmaceutical industry, these ML global models are available across discovery projects as an "out-of-the-box" solution to assist in drug design, synthesis prioritization, and experiment selection.

Systematic Evaluation of Local and Global Machine Learning Models for the Prediction of ADME Properties.

Machine learning (ML) has become an indispensable tool to predict absorption, distribution, metabolism, and excretion (ADME) properties in pharmaceutical research. ML algorithms are trained on molecular structures and corresponding ADME assay data to develop quantitative structure-property relationship (QSPR) models. Traditional QSPR models were trained on compound sets of limited size.

Multispecies Machine Learning Predictions of In Vitro Intrinsic Clearance with Uncertainty Quantification Analyses.

In pharmaceutical research, compounds are optimized for metabolic stability to avoid a too fast elimination of the drug. Intrinsic clearance (CL) measured in liver microsomes or hepatocytes is an important parameter during lead optimization. In this work, machine learning models were developed to relate the compound structure to microsomal metabolic stability and predict CL for new compounds.

Predicting In Vivo Compound Brain Penetration Using Multi-task Graph Neural Networks.

Assessing whether compounds penetrate the brain can become critical in drug discovery, either to prevent adverse events or to reach the biological target. Generally, pre-clinical in vivo studies measuring the ratio of brain and blood concentrations () are required to estimate the brain penetration potential of a new drug entity. In this work, we developed machine learning models to predict in vivo compound brain penetration (as Log) from chemical structure.

Chemical Reactivity Prediction: Current Methods and Different Application Areas.

The ability to predict chemical reactivity of a molecule is highly desirable in drug discovery, both ex vivo (synthetic route planning, formulation, stability) and in vivo: metabolic reactions determine pharmacodynamics, pharmacokinetics and potential toxic effects, and early assessment of liabilities is vital to reduce attrition rates in later stages of development. Quantum mechanics offer a precise description of the interactions between electrons and orbitals in the breaking and forming of new bonds. Modern algorithms and faster computers have allowed the study of more complex systems in a punctual and accurate fashion, and answers for chemical questions around stability and reactivity can now be provided.

Safety screening in early drug discovery: An optimized assay panel.

Background: Several factors contribute to the development failure of novel pharmaceuticals, one of the most important being adverse effects in pre-clinical and clinical studies. Early identification of off-target compound activity can reduce safety-related attrition in development. In vitro profiling of drug candidates against a broad range of targets is an important part of the compound selection process.

Can we discover pharmacological promiscuity early in the drug discovery process?

The term 'pharmacological promiscuity' describes the activity of a single compound against multiple targets. When undesired, promiscuity is a major safety concern that needs to be detected as early as possible in the drug discovery process. The analysis of large datasets reveals that the majority of promiscuous compounds are characterized by recognizable molecular properties and structural motifs, the most important one being a basic center with a pK(a)(B)>6.

Coexistence of passive and carrier-mediated processes in drug transport.

The permeability of biological membranes is one of the most important determinants of the pharmacokinetic processes of a drug. Although it is often accepted that many drug substances are transported across biological membranes by passive transcellular diffusion, a recent hypothesis speculated that carrier-mediated mechanisms might account for the majority of membrane drug transport processes in biological systems. Based on evidence of the physicochemical characteristics and of in vitro and in vivo findings for marketed drugs, as well as results from real-life discovery and development projects, we present the view that both passive transcellular processes and carrier-mediated processes coexist and contribute to drug transport activities across biological membranes.

In silico prediction of blood-brain barrier permeation using the calculated molecular cross-sectional area as main parameter.

The cross-sectional area, AD, of a compound oriented in an amphiphilic gradient such as the air-water or lipid-water interface has previously been shown to be crucial for membrane partitioning and permeation, respectively. Here, we developed an algorithm that determines the molecular axis of amphiphilicity and the cross-sectional area, ADcalc, perpendicular to this axis. Starting from the conformational ensemble of each molecule, the three-dimensional conformation selected as the membrane-binding conformation was the one with the smallest cross-sectional area, ADcalcM, and the strongest amphiphilicity.

Enhancement of drug absorption by noncharged detergents through membrane and P-glycoprotein binding.

Noncharged detergents are used as excipients in drug formulations. Until recently, they were considered as inert compounds, enhancing drug absorption essentially by improving drug solubility. However, many detergents insert into lipid membranes, although to different extents, and change the lateral packing density of membranes at high concentrations.

Halogenation of drugs enhances membrane binding and permeation.

Halogenation of drugs is commonly used to enhance membrane binding and permeation. We quantify the effect of replacing a hydrogen residue by a chlorine or a trifluoromethyl residue in position C-2 of promazine, perazine, and perphenazine analogues. Moreover, we investigate the influence of the position (C-6 and C-7) of residue CF(3) in benzopyranols.

Design and synthesis of novel imidazo[1,2-a]quinoxalines as PDE4 inhibitors.

New imidazo[1,2-a]quinoxaline derivatives have been synthesised by condensation of an appropriate alpha-aminoalcohol with a quinoxaline followed by intramolecular cyclisation and nucleophilic substitutions. Their phosphodiesterase inhibitory activities have been assessed on a preparation of the PDE4 isoform purified from a human alveolar epithelial cell line (A549). These studies showed potent inhibitory properties that emphasize the importance of a methyl amino group at position 4 and a weakly hindered group at position 1.