Bayer's in silico ADMET platform: a journey of machine learning over the past two decades.

Over the past two decades, an in silico absorption, distribution, metabolism, and excretion (ADMET) platform has been created at Bayer Pharma with the goal to generate models for a variety of pharmacokinetic and physicochemical endpoints in early drug discovery. These tools are accessible to all scientists within the company and can be a useful in assisting with the selection and design of novel leads, as well as the process of lead optimization. Here.

Discovery of Rogaratinib (BAY 1163877): a pan-FGFR Inhibitor.

Rogaratinib (BAY 1163877) is a highly potent and selective small-molecule pan-fibroblast growth factor receptor (FGFR) inhibitor (FGFR1-4) for oral application currently being investigated in phase 1 clinical trials for the treatment of cancer. In this publication, we report its discovery by de novo structure-based design and medicinal chemistry optimization together with its pharmacokinetic profile.

Novel TIE-2 inhibitor BAY-826 displays in vivo efficacy in experimental syngeneic murine glioma models.

Targeting the vascular endothelial growth factor signaling axis in glioblastoma inevitably leads to tumor recurrence and a more aggressive phenotype. Therefore, other angiogenic pathways, like the angiopoietin/tunica interna endothelial cell kinase (TIE) signaling axis, have become additional targets for therapeutic intervention. Here, we explored whether targeting the receptor tyrosine kinase TIE-2 using a novel, highly potent, orally available small molecule TIE-2 inhibitor (BAY-826) improves tumor control in syngeneic mouse glioma models.

Best of both worlds: combining pharma data and state of the art modeling technology to improve in Silico pKa prediction.

In a unique collaboration between a software company and a pharmaceutical company, we were able to develop a new in silico pKa prediction tool with outstanding prediction quality. An existing pKa prediction method from Simulations Plus based on artificial neural network ensembles (ANNE), microstates analysis, and literature data was retrained with a large homogeneous data set of drug-like molecules from Bayer. The new model was thus built with curated sets of ∼14,000 literature pKa values (∼11,000 compounds, representing literature chemical space) and ∼19,500 pKa values experimentally determined at Bayer Pharma (∼16,000 compounds, representing industry chemical space).

CypScore: Quantitative prediction of reactivity toward cytochromes P450 based on semiempirical molecular orbital theory.

CypScore is an in silico approach for predicting the likely sites of cytochrome P450-mediated metabolism of druglike organic molecules. It consists of multiple models for the most important P450 oxidation reactions such as aliphatic hydroxylation, N-dealkylation, O-dealkylation, aromatic hydroxylation, double-bond oxidation, N-oxidation, and S-oxidation. Each of these models is based on atomic reactivity descriptors derived from surface-based properties calculated with ParaSurf and based on AM1 semiempirical molecular orbital theory.

Design and synthesis of potent and selective azaindole-based Rho kinase (ROCK) inhibitors.

Rho kinase plays a pivotal role in several cellular processes such as vasoregulation, making it a suitable target for the treatment of hypertension and related disorders. We discovered a new compound class of Rho kinase (ROCK) inhibitors containing a 7-azaindole hinge-binding scaffold tethered to an aminopyrimidine core. Herein we describe the structure-activity relationships elucidated through biochemical and functional assays.

In silico ADMET traffic lights as a tool for the prioritization of HTS hits.

The need for in silico characterization of HTS hit structures as part of a data-driven hit-selection process is demonstrated. A solution is described in the form of an in silico ADMET traffic light and PhysChem scoring system. This has been extensively validated with in-house data at Bayer, published data, and a collection of launched small-molecule oral drugs.

Improving the hit-to-lead process: data-driven assessment of drug-like and lead-like screening hits.

Drug-like and lead-like hits derived from HTS campaigns provide good starting points for lead optimization. However, too strong emphasis on potency as hit-selection parameter might hamper the success of such projects. A detailed absorption, distribution, metabolism, excretion and toxicology (ADME-Tox) profiling is needed to help identify hits with a minimum number of (known) liabilities.

In silico prediction of aqueous solubility, human plasma protein binding and volume of distribution of compounds from calculated pKa and AlogP98 values.

We have investigated whether three important ADME (absorption, distribution, metabolism, excretion) related properties (aqueous solubility, human plasma protein binding, and human volume of distribution at steady-state) can be predicted from chemical structure alone if only the predicted predominant ionisation state and lipophilicity (calculated logP [P = octanol-water partition coefficient]) are considered. A simple, fast method for the in silico prediction of aqueous solubility of predominantly uncharged compounds has been developed, while some potential is shown for the prediction of predominantly charged or zwitterionic compounds. Ten other known in silico prediction methods for aqueous solubility have also been evaluated.

Molecular characterization of the principal substrate binding site of the ubiquitous folding catalyst protein disulfide isomerase.

Disulfide bond formation in the endoplasmic reticulum of eukaryotes is catalyzed by the ubiquitously expressed enzyme protein disulfide isomerase (PDI). The effectiveness of PDI as a catalyst of native disulfide bond formation in folding polypeptides depends on the ability to catalyze disulfide-dithiol exchange, to bind non-native proteins, and to trigger conformational changes in the bound substrate, allowing access to buried cysteine residues. It is known that the b' domain of PDI provides the principal peptide binding site of PDI and that this domain is critical for catalysis of isomerization but not oxidation reactions in protein substrates.

Recent advances in the prediction of blood-brain partitioning from molecular structure.

The widely distributed software tools Cerius2 and ACD/log D Suite have been used to develop a new method for the prediction of the ratio of concentrations of a drug in the brain and blood (BB,quantified as log BB) from structure. The performances of all known blood-brain partitioning prediction methods are compared to give an up-to-date account on their accuracy, limitations, and usefulness. It is demonstrated that the new log BB prediction method is superior to other methods with regard to low-to-medium throughput log BB prediction, whereas the C2-ADME log BB two-dimensional (2D) method seems to offer the best compromise between speed and accuracy for ultra-high throughput processing of large compound databases for log BB prediction.