Herman van Vlijmen

Extending kinome coverage by analysis of kinase inhibitor broad profiling data

The explored kinome was extended with broad profiling using the DiscoveRx and Millipore assay panels. The analysis of the profiling of 3368 selected inhibitors on 456 kinases in the DiscoveRx format delivered several insights. First, the coverage depended on the threshold of the selectivity parameter. Second, the relation between hit confirmation rates and inhibitor selectivity showed unexpectedly that higher selectivity can increase the likelihood of false positives. Third, comparing the coverage of a focused to that of a random library showed that the design based on a maximum number of scaffolds was superior to a limited number of scaffolds. Therefore, selective compounds can be used in target validation, enable the jumpstarting of new kinase drug discovery projects, and chart new biological space via phenotypic screening.

Collaborating to improve the use of free-energy and other quantitative methods in drug discovery

In May and August, 2016, several pharmaceutical companies convened to discuss and compare experiences with Free Energy Perturbation (FEP). This unusual synchronization of interest was prompted by Schrödinger’s FEP+ implementation and offered the opportunity to share fresh studies with FEP and enable broader discussions on the topic. This article summarizes key conclusions of the meetings, including a path forward of actions for this group to aid the accelerated evaluation, application and development of free energy and related quantitative, structure-based design methods.

Suzuki–Miyaura Diversification of Amino Acids and Dipeptides in Aqueous Media

The Suzuki–Miyaura derivatisation of free amino acids, peptides and proteins is an attractive area with considerable potential utility for medicinal chemistry and chemical biology. Here we report the modification of unprotected and Boc‐protected aromatic amino acids and dipeptides in aqueous media, enabling heteroarylation and vinylation. We systematically investigate the impact of the peptide backbone and adjacent amino acid residues upon the reaction. Our studies reveal that although asparagine and histidine hinder the reaction, by utilising dppf, a ferrocene‐based bidentate phosphine ligand, cross coupling of halophenylalanine or halotryptophan adjacent to such a residue could be enabled. Our studies reveal dppf to have good compatibility with all unprotected, proteinogenic amino acid side chains.

Computational chemistry at Janssen

Computer-aided drug discovery activities at Janssen are carried out by scientists in the Computational Chemistry group of the Discovery Sciences organization. This perspective gives an overview of the organizational and operational structure, the science, internal and external collaborations, and the impact of the group on Drug Discovery at Janssen.

Blood-based metabolic signatures in Alzheimer's disease.

Identification of blood‐based metabolic changes might provide early and easy‐to‐obtain biomarkers.

Protocols for the Design of Kinase-Focused Compound Libraries

Protocols for the design of kinase‐focused compound libraries are presented. Kinase‐focused compound libraries can be differentiated based on the design goal. Depending on whether the library should be a discovery library specific for one particular kinase, a general discovery library for multiple distinct kinase projects, or even phenotypic screening, there exists today a variety of in silico methods to design candidate compound libraries. We address the following scenarios: 1) Datamining of SAR databases and kinase focused vendor catalogues; 2) Predictions and virtual screening; 3) Structure‐based design of combinatorial kinase inhibitors; 4) Design of covalent kinase inhibitors; 5) Design of macrocyclic kinase inhibitors; and 6) Design of allosteric kinase inhibitors and activators.

Molecular Modeling of Drug–Transporter Interactions—An International Transporter Consortium Perspective

Membrane transporters play diverse roles in the pharmacokinetics and pharmacodynamics of small‐molecule drugs. Understanding the mechanisms of drug‐transporter interactions at the molecular level is, therefore, essential for the design of drugs with optimal therapeutic effects. This white paper examines recent progress, applications, and challenges of molecular modeling of membrane transporters, including modeling techniques that are centered on the structures of transporter ligands, and those focusing on the structures of the transporters. The goals of this article are to illustrate current best practices and future opportunities in using molecular modeling techniques to understand and predict transporter‐mediated effects on drug disposition and efficacy.Membrane transporters from the solute carrier (SLC) and ATP‐binding cassette (ABC) superfamilies regulate the cellular uptake, efflux, and homeostasis of many essential nutrients and significantly impact the pharmacokinetics of drugs ; further, they may provide targets for novel therapeutics as well as facilitate prodrug approaches. Because of their often broad substrate selectivity they are also implicated in many undesirable and sometimes life‐threatening drug–drug interactions (DDIs).5,6

Multi-model inference using mixed effects from a linear regression based genetic algorithm

BackgroundDifferent high-dimensional regression methodologies exist for the selection of variables to predict a continuous variable. To improve the variable selection in case clustered observations are present in the training data, an extension towards mixed-effects modeling (MM) is requested, but may not always be straightforward to implement.In this article, we developed such a MM extension (GA-MM-MMI) for the automated variable selection by a linear regression based genetic algorithm (GA) using multi-model inference (MMI). We exemplify our approach by training a linear regression model for prediction of resistance to the integrase inhibitor Raltegravir (RAL) on a genotype-phenotype database, with many integrase mutations as candidate covariates. The genotype-phenotype pairs in this database were derived from a limited number of subjects, with presence of multiple data points from the same subject, and with an intra-class correlation of 0.92.ResultsIn generation of the RAL model, we took computational efficiency into account by optimizing the GA parameters one by one, and by using tournament selection. To derive the main GA parameters we used 3 times 5-fold cross-validation. The number of integrase mutations to be used as covariates in the mixed effects models was 25 (chrom.size). A GA solution was found when R2MM > 0.95 (goal.fitness). We tested three different MMI approaches to combine the results of 100 GA solutions into one GA-MM-MMI model. When evaluating the GA-MM-MMI performance on two unseen data sets, a more parsimonious and interpretable model was found (GA-MM-MMI TOP18: mixed-effects model containing the 18 most prevalent mutations in the GA solutions, refitted on the training data) with better predictive accuracy (R2) in comparison to GA-ordinary least squares (GA-OLS) and Least Absolute Shrinkage and Selection Operator (LASSO).ConclusionsWe have demonstrated improved performance when using GA-MM-MMI for selection of mutations on a genotype-phenotype data set. As we largely automated setting the GA parameters, the method should be applicable on similar datasets with clustered observations.

Large-Scale Validation of Mixed-Solvent Simulations to Assess Hotspots at Protein–Protein Interaction Interfaces

The ability to target protein-protein interactions (PPIs) with small molecule inhibitors offers great promise in expanding the druggable target space and addressing a broad range of untreated diseases. However, due to their nature and function of interacting with protein partners, PPI interfaces tend to extend over large surfaces without the typical pockets of enzymes and receptors. These features present unique challenges for small molecule inhibitor design. As such, determining whether a particular PPI of interest could be pursued with a small molecule discovery strategy requires an understanding of the characteristics of the PPI interface and whether it has hotspots that can be leveraged by small molecules to achieve desired potency. Here, we assess the ability of mixed-solvent molecular dynamic (MSMD) simulations to detect hotspots at PPI interfaces. MSMD simulations using three cosolvents (acetonitrile, isopropanol, and pyrimidine) were performed on a large test set of 21 PPI targets that have been experimentally validated by small molecule inhibitors. We compare MSMD, which includes explicit solvent and full protein flexibility, to a simpler approach that does not include dynamics or explicit solvent (SiteMap) and find that MSMD simulations reveal additional information about the characteristics of these targets and the ability for small molecules to inhibit the PPI interface. In the few cases were MSMD simulations did not detect hotspots, we explore the shortcomings of this technique and propose future improvements. Finally, using Interleukin-2 as an example, we highlight the advantage of the MSMD approach for detecting transient cryptic druggable pockets that exists at PPI interfaces.

PROFILING PERIPHERAL METABOLIC DYSREGULATION IN ALZHEIMER’S DISEASE: THE ADDED VALUE OF MULTIPLE SIGNATURES

actual mechanism in AD pathogenesis is poorly understood. Methods: In this cross-sectional comparison, we examined the plasma LDL lipoprotein profiles of clinically classified healthy control and Alzheimer’s affected individuals from the Australian Imaging, Biomarker and Lifestyle (AIBL) study. These individuals had also undergone several other characterisations including PiBPET imaging for determination of brain amyloid load. Results: The data indicate that the APOE ε4 participants tended to have higher median levels of larger plasma LDL species while also being associated with higher amyloid load. These individuals also showed high variations in large LDL levels, indicating altered regulation of LDL metabolism. We also identified a possible association with LDL levels in non-ε4 participants and neocortical amyloid load, suggesting that plasma LDL is involved with AD pathogenesis. Conclusions:APOE ε4 influences the levels of plasma LDL species and is associated with higher amyloid load in the brain. The larger LDL species show positive association with higher brain amyloid load, supporting a metabolic link between brain and periphery.