Inhibitor selectivity: profiling and prediction

Less than 1 in 10 drug candidates that enter phase 1 clinical trials actually gets approved for human use. The high failure rate is in part due to unforeseen side effects or toxicity. A better understanding of the role of selectivity and a better insight in the off-target activities of drug candidates could greatly aid in preventing candidates to fail for these reasons. This thesis has tried to address some aspects in this challenging part of drug discovery. The use of activity-based protein profiling as presented in Chapters 2 and 3 in drug discovery and hit-to-lead optimization, and in Chapter 5 and 6 for the interaction profiling of a drug candidate, highlights the versatility and importance of this chemical biology technique. Combined with knowledge derived from biochemical assays, such as that developed in Chapter 4, ABPP can greatly aid the medicinal chemist. The recent surge in popularity of machine learning algorithms, backed by exponential growth of the amount of biological data available, holds great promise for drug discovery. Chapters 7 and 8 showed the applicability of one such algorithm, which was able to quite reliably predict interaction profiles. The challenges in finding, determining and predicting selectivity are far from solved, but, by incrementally expanding our understanding of the binding of small molecules to their (off-)targets, truly selective inhibitors might at some point become a reality or their necessity might be mitigated.Medical Biochemistr

Drug Discovery Maps, a Machine Learning Model That Visualizes and Predicts Kinome-Inhibitor Interaction Landscapes

The interpretation of high-dimensional structure-activity data sets in drug discovery to predict ligand-protein interaction landscapes is a challenging task. Here we present Drug Discovery Maps (DDM), a machine learning model that maps the activity profile of compounds across an entire protein family, as illustrated here for the kinase family. DDM is based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm to generate a visualization of molecular and biological similarity. DDM maps chemical and target space and predicts the activities of novel kinase inhibitors across the kinome. The model was validated using independent data sets and in a prospective experimental setting, where DDM predicted new inhibitors for FMS-like tyrosine kinase 3 (FLT3), a therapeutic target for the treatment of acute myeloid leukemia. Compounds were resynthesized, yielding highly potent, cellularly active FLT3 inhibitors. Biochemical assays confirmed most of the predicted off-targets. DDM is further unique in that it is completely open-source and available as a ready-to-use executable to facilitate broad and easy adoption

Drug Discovery Maps, a Machine Learning Model That Visualizes and Predicts Kinome-Inhibitor Interaction Landscapes

The interpretation of high-dimensional structure-activity data sets in drug discovery to predict ligand-protein interaction landscapes is a challenging task. Here we present Drug Discovery Maps (DDM), a machine learning model that maps the activity profile of compounds across an entire protein family, as illustrated here for the kinase family. DDM is based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm to generate a visualization of molecular and biological similarity. DDM maps chemical and target space and predicts the activities of novel kinase inhibitors across the kinome. The model was validated using independent data sets and in a prospective experimental setting, where DDM predicted new inhibitors for FMS-like tyrosine kinase 3 (FLT3), a therapeutic target for the treatment of acute myeloid leukemia. Compounds were resynthesized, yielding highly potent, cellularly active FLT3 inhibitors. Biochemical assays confirmed most of the predicted off-targets. DDM is further unique in that it is completely open-source and available as a ready-to-use executable to facilitate broad and easy adoption

Drug Discovery Maps, a Machine Learning Model That Visualizes and Predicts Kinome-Inhibitor Interaction Landscapes

The interpretation of high-dimensional structure-activity data sets in drug discovery to predict ligand-protein interaction landscapes is a challenging task. Here we present Drug Discovery Maps (DDM), a machine learning model that maps the activity profile of compounds across an entire protein family, as illustrated here for the kinase family. DDM is based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm to generate a visualization of molecular and biological similarity. DDM maps chemical and target space and predicts the activities of novel kinase inhibitors across the kinome. The model was validated using independent data sets and in a prospective experimental setting, where DDM predicted new inhibitors for FMS-like tyrosine kinase 3 (FLT3), a therapeutic target for the treatment of acute myeloid leukemia. Compounds were resynthesized, yielding highly potent, cellularly active FLT3 inhibitors. Biochemical assays confirmed most of the predicted off-targets. DDM is further unique in that it is completely open-source and available as a ready-to-use executable to facilitate broad and easy adoption

Oncological drug discovery: AI meets structure-based computational research

The integration of machine learning and structure-based methods has proven valuable in the past as a way to prioritize targets and compounds in early drug discovery. In oncological research, these methods can be highly beneficial in addressing the diversity of neoplastic diseases portrayed by the different hallmarks of cancer. Here, we review six use case scenarios for integrated computational methods, namely driver prediction, computational mutagenesis, (off)-target prediction, binding site prediction, virtual screening, and allosteric modulation analysis. We address the heterogeneity of integration approaches and individual methods, while acknowledging their current limitations and highlighting their potential to bring drugs for personalized oncological therapies to the market faster.Medicinal Chemistr

Structure Kinetics Relationships and Molecular Dynamics Show Crucial Role for Heterocycle Leaving Group in Irreversible Diacylglycerol Lipase Inhibitors

Drug discovery programs of covalent irreversible, mechanism-based enzyme inhibitors often focus on optimization of potency as determined by IC50-values in biochemical assays. These assays do not allow the characterization of the binding activity (Ki) and reactivity (kinact) as individual kinetic parameters of the covalent inhibitors. Here, we report the development of a kinetic substrate assay to study the influence of the acidity (pKa) of heterocyclic leaving group of triazole urea derivatives as diacylglycerol lipase (DAGL)-α inhibitors. Surprisingly, we found that the reactivity of the inhibitors did not correlate with the pKa of the leaving group, whereas the position of the nitrogen atoms in the heterocyclic core determined to a large extent the binding activity of the inhibitor. This finding was confirmed and clarified by molecular dynamics simulations on the covalently bound Michaelis−Menten complex. A deeper understanding of the binding properties of covalent serine hydrolase inhibitors is expected to aid in the discovery and development of more selective covalent inhibitors.Medicinal Chemistr

Two-step activity-based protein profiling of diacylglycerol lipase

Diacylglycerol lipases (DAGL) produce the endocannabinoid 2-arachidonoylglycerol, a key modulator of neurotransmitter release. Chemical tools that visualize endogenous DAGL activity are desired. Here, we report the design, synthesis and application of a triazole urea probe for DAGL equipped with a norbornene as a biorthogonal handle. The activity and selectivity of the probe was assessed with activity-based protein profiling. This probe was potent against endogenous DAGLα (IC50 = 5 nM) and it was successfully applied as a two-step activity-based probe for labeling of DAGLα using an inverse electron-demand Diels–Alder ligation in living cells.Bio-organic SynthesisMolecular Physiolog

Chemical proteomics reveals antibiotic targets of oxadiazolones in MRSA

Molecular PhysiologyBio-organic Synthesi

Activity-based protein profiling reveals off-target proteins of the FAAH inhibitor BIA 10-2474

A recent phase 1 trial of the fatty acid amide hydrolase (FAAH) inhibitor BIA 10-2474 led to the death of one volunteer and produced mild-to-severe neurological symptoms in four others. Although the cause of the clinical neurotoxicity is unknown, it has been postulated, given the clinical safety profile of other tested FAAH inhibitors, that off-target activities of BIA 10-2474 may have played a role. Here we use activity-based proteomic methods to determine the protein interaction landscape of BIA 10-2474 in human cells and tissues. This analysis revealed that the drug inhibits several lipases that are not targeted by PF04457845, a highly selective and clinically tested FAAH inhibitor. BIA 10-2474, but not PF04457845, produced substantial alterations in lipid networks in human cortical neurons, suggesting that promiscuous lipase inhibitors have the potential to cause metabolic dysregulation in the nervous system.Bio-organic SynthesisMolecular Physiolog