Exploring Selectivity of Multikinase Inhibitors across the Human Kinome

Target-specific compound selectivity for multi-target drug discovery and repurposing

Most drug molecules modulate multiple target proteins, leading either to therapeutic effects or unwanted side effects. Such target promiscuity partly contributes to high attrition rates and leads to wasted costs and time in the current drug discovery process, and makes the assessment of compound selectivity an important factor in drug development and repurposing efforts. Traditionally, selectivity of a compound is characterized in terms of its target activity profile (wide or narrow), which can be quantified using various statistical and information theoretic metrics. Even though the existing selectivity metrics are widely used for characterizing the overall selectivity of a compound, they fall short in quantifying how selective the compound is against a particular target protein (e.g., disease target of interest). We therefore extended the concept of compound selectivity towards target-specific selectivity, defined as the potency of a compound to bind to the particular protein in comparison to the other potential targets. We decompose the target-specific selectivity into two components: 1) the compound’s potency against the target of interest (absolute potency), and 2) the compound’s potency against the other targets (relative potency). The maximally selective compound-target pairs are then identified as a solution of a bi-objective optimization problem that simultaneously optimizes these two potency metrics. In computational experiments carried out using large-scale kinase inhibitor dataset, which represents a wide range of polypharmacological activities, we show how the optimization-based selectivity scoring offers a systematic approach to finding both potent and selective compounds against given kinase targets. Compared to the existing selectivity metrics, we show how the target-specific selectivity provides additional insights into the target selectivity and promiscuity of multi-targeting kinase inhibitors. Even though the selectivity score is shown to be relatively robust against both missing bioactivity values and the dataset size, we further developed a permutation-based procedure to calculate empirical p-values to assess the statistical significance of the observed selectivity of a compound-target pair in the given bioactivity dataset. We present several case studies that show how the target-specific selectivity can distinguish between highly selective and broadly-active kinase inhibitors, hence facilitating the discovery or repurposing of multi-targeting drugs.

Indolin-2-one derivatives as selective Aurora B kinase inhibitors targeting breast cancer

Aurora B is a pivotal cell cycle regulator where errors in its function results in polyploidy, genetic instability, and tumorigenesis. It is overexpressed in many cancers, consequently, targeting Aurora B with small molecule inhibitors constitutes a promising approach for anticancer therapy. Guided by structure-based design and molecular hybridization approach we developed a series of fifteen indolin-2-one derivatives based on a previously reported indolin-2-one-based multikinase inhibitor (1). Seven derivatives, 5g, 6a, 6c-e, 7, and 8a showed preferential antiproliferative activity in NCI-60 cell line screening and out of these, carbamate 6e and cyclopropylurea 8a derivatives showed optimum activity against Aurora B (IC₅₀ = 16.2 and 10.5 nM respectively) and MDA-MB-468 cells (IC₅₀ = 32.6 ± 9.9 and 29.1 ± 7.3 nM respectively). Furthermore, 6e and 8a impaired the clonogenic potential of MDA-MB-468 cells. Mechanistic investigations indicated that 6e and 8a induced G2/M cell cycle arrest, apoptosis, and necrosis of MDA-MB-468 cells and western blot analysis of 8a effect on MDA-MB-468 cells revealed 8a's ability to reduce Aurora B and its downstream target, Histone H3 phosphorylation. 6e and 8a displayed better safety profiles than multikinase inhibitors such as sunitinib, showing no cytotoxic effects on normal rat cardiomyoblasts and murine hepatocytes. Finally, 8a demonstrated a more selective profile than 1 when screened against ten related kinases. Based on these findings, 8a represents a promising candidate for further development to target breast cancer via Aurora B selective inhibition.

Fragment-based screening and hit-based substructure search: Rapid discovery of 8-hydroxyquinoline-7-carboxylic acid as a low-cytotoxic, nanomolar metallo β-lactamase inhibitor

Metallo-β-lactamases (MBLs) are zinc-containing carbapenemases that inactivate a broad range of β-lactam antibiotics. There is a lack of β-lactamase inhibitors for restoring existing β-lactam antibiotics arsenals against common bacterial infections. Fragment-based screening of a non-specific metal chelator library demonstrates 8-hydroxyquinoline as a broad-spectrum nanomolar inhibitor against VIM-2 and NDM-1. A hit-based substructure search provided an early structure-activity relationship of 8-hydroxyquinolines and identified 8-hydroxyquinoline-7-carboxylic acid as a low-cytotoxic β-lactamase inhibitor that can restore β-lactam activity against VIM-2-expressing E. coli. Molecular modeling further shed structural insight into its potential mode of binding within the dinuclear zinc active site. 8-Hydroxyquinoline-7-carboxylic acid is highly stable in human plasma and human liver microsomal study, making it an ideal lead candidate for further development.

Assessing the information content of structural and protein-ligand interaction representations for the classification of kinase inhibitor binding modes via machine learning and active learning

For kinase inhibitors, X-ray crystallography has revealed different types of binding modes. Currently, more than 2000 kinase inhibitors with known binding modes are available, which makes it possible to derive and test machine learning models for the prediction of inhibitors with different binding modes. We have addressed this prediction task to evaluate and compare the information content of distinct molecular representations including protein–ligand interaction fingerprints (IFPs) and compound structure-based structural fingerprints (i.e., atom environment/fragment fingerprints). IFPs were designed to capture binding mode-specific interaction patterns at different resolution levels. Accurate predictions of kinase inhibitor binding modes were achieved with random forests using both representations. The performance of IFPs was consistently superior to atom environment fingerprints, albeit only by less than 10%. An active learning strategy applying information entropy-based selection of training instances was applied as a diagnostic approach to assess the relative information content of distinct representations. IFPs were found to capture more binding mode-relevant information than atom environment fingerprints, leading to highly predictive models even when training instances were randomly selected. By contrast, for atom environment fingerprints, the derivation of accurate models via active learning depended on entropy-based selection of informative training compounds. Notably, higher information content of IFPs confirmed by active learning only resulted in small improvements in global prediction accuracy compared to models derived using atom environment fingerprints. For practical applications, prediction of binding modes of new kinase inhibitors on the basis of chemical structure is highly attractive.

Structural analysis of fungal pathogenicity-related casein kinase α subunit, Cka1, in the human fungal pathogen Cryptococcus neoformans

CK2α is a constitutively active and highly conserved serine/threonine protein kinase that is involved in the regulation of key cellular metabolic pathways and associated with a variety of tumours and cancers. The most well-known CK2α inhibitor is the human clinical trial candidate CX-4945, which has recently shown to exhibit not only anti-cancer, but also anti-fungal properties. This prompted us to work on the CK2α orthologue, Cka1, from the pathogenic fungus Cryptococcus neoformans, which causes life-threatening systemic cryptococcosis and meningoencephalitis mainly in immunocompromised individuals. At present, treatment of cryptococcosis remains a challenge due to limited anti-cryptococcal therapeutic strategies. Hence, expanding therapeutic options for the treatment of the disease is highly clinically relevant. Herein, we report the structures of Cka1-AMPPNP-Mg²⁺ (2.40 Å) and Cka1-CX-4945 (2.09 Å). Structural comparisons of Cka1-AMPPNP-Mg²⁺ with other orthologues revealed the dynamic architecture of the N-lobe across species. This may explain for the difference in binding affinities and deviations in protein-inhibitor interactions between Cka1-CX-4945 and human CK2α-CX-4945. Supporting it, in vitro kinase assay demonstrated that CX-4945 inhibited human CK2α much more efficiently than Cka1. Our results provide structural insights into the design of more selective inhibitors against Cka1.

Data structures for compound promiscuity analysis: promiscuity cliffs, pathways and promiscuity hubs formed by inhibitors of the human kinome

AIM

A large collection of promiscuity cliffs (PCs), PC pathways (PCPs) and promiscuity hubs (PHs) formed by inhibitors of human kinases is made freely available.

METHODOLOGY

Inhibitor PCs were systematically identified and organized in network representations, from which PCPs were extracted. PH compounds were classified and their neighborhoods analyzed.

DATA & EXEMPLARY RESULTS

Nearly 16,000 PCs covering the human kinome were identified, which yielded more than 600 PC clusters and 8900 PCPs. Moreover, 520 PHs were obtained.

LIMITATIONS & NEXT STEPS

PC and PCP data structures capture structure-promiscuity relationships. Promiscuity assessment is also affected by data sparseness. Given the rapid growth of kinase inhibitor data, the relevance of PC/PCP/PH information for medicinal chemistry and chemical biology applications will further increase.

Data-Driven Exploration of Selectivity and Off-Target Activities of Designated Chemical Probes

Chemical probes are of central relevance for chemical biology. To unambiguously explore the role of target proteins in triggering or mediating biological functions, small molecules used as probes should ideally be target-specific; at least, they should have sufficiently high selectivity for a primary target. We present a thorough analysis of currently available activity data for designated chemical probes to address several key questions: How well defined are chemical probes? What is their level of selectivity? Is there evidence for additional activities? Are some probes "better" than others? Therefore, highly curated chemical probes were collected and their selectivity was analyzed on the basis of publicly available compound activity data. Different selectivity patterns were observed, which distinguished designated high-quality probes.

Reconciling Selectivity Trends from a Comprehensive Kinase Inhibitor Profiling Campaign with Known Activity Data

Kinase inhibitors are among the most intensely investigated compounds in medicinal chemistry and drug development. Profiling experiments and kinome screens reveal binding characteristics of kinase inhibitors and lead to better understanding of selectivity and promiscuity patterns. However, only limited amounts of profiling data are publicly available. By contrast, a large body of activity data for inhibitors of human kinases has become available from medicinal chemistry. In this study, we have correlated selectivity assessment of clinical kinase inhibitors from the most comprehensive profiling campaign reported to date with systematic mining of activity data from other sources. The results of our comparative analysis reveal consistency of orthogonal approaches in the study of kinase inhibitor selectivity versus promiscuity and stress the importance of taking alternative data confidence criteria into account. Moreover, it is also shown that there are little if any detectable differences in selectivity between type I and II kinase inhibitors and that inhibitors designated as chemical probes have very different target profiles.

Evaluation of Kinase Inhibitor Selectivity Using Cell-based Profiling Data

Kinases are among the most heavily investigated drug targets and inhibition of kinases and kinase-dependent signaling has become a paradigm for therapeutic intervention. Kinase inhibitors and associated activity data have increasing 'big data' character, which presents challenges for computational analysis, but also unprecedented opportunities for learning from compound data and for data-driven medicinal chemistry. Herein, publicly available kinase inhibitor data are evaluated and a number of characteristics are discussed. In addition, selectivity of clinical kinase inhibitors is explored computationally on the basis of recently reported cell-based profiling data. For inhibitors shared by pairs of kinases, selectivity profiles were generated and a variety of selective inhibitors were identified. Uni-directional selectivity profiles revealed inhibitors that were selective for one kinase over the other, while bi-directional profiles uncovered compounds with inverted selectivity for paired kinases.