Abstract
Polypharmacology plays an important role in defining response and adverse effects of drugs. For some mechanisms, experimentally mapping polypharmacology is commonplace, although this is typically done within the same protein class. Four PARP inhibitors have been approved by the FDA as cancer therapeutics, yet a precise mechanistic rationale to guide clinicians on which to choose for a particular patient is lacking. The four drugs have largely similar PARP family inhibition profiles, but several differences at the molecular and clinical level have been reported that remain poorly understood. Here, we report the first comprehensive characterization of the off-target kinase landscape of four FDA-approved PARP drugs. We demonstrate that all four PARP inhibitors have a unique polypharmacological profile across the kinome. Niraparib and rucaparib inhibit DYRK1s, CDK16 and PIM3 at clinically achievable, submicromolar concentrations. These kinases represent the most potently inhibited off-targets of PARP inhibitors identified to date and should be investigated further to clarify their potential implications for efficacy and safety in the clinic. Moreover, broad kinome profiling is recommended for the development of PARP inhibitors as PARP-kinase polypharmacology could potentially be exploited to modulate efficacy and side-effect profiles.
Highlights
It is widely accepted that drugs often bind several proteins beyond their intended target, which has implications for both therapeutic efficacy and adverse-effects
We applied three parallel computational methods to predict off-targets: (1) a consensus of six ligand-based chemoinformatic methods integrated in the Chemotargets CLARITY platform[34]; (2) the Similarity Ensemble Approach (SEA)[7]; and (3) the multinomial Naive Bayesian multi-category scikit-learn method implemented in ChEMBL35
A close inspection of the CLARITY predictions revealed that they were all generated from the similarity of olaparib to a single kinase inhibitor that was likely to be a false positive due to the absence within its structure of a benzamide moiety, which is known to be important for poly (ADP-ribose) polymerase (PARP) binding (Supplementary Table 2)[15]
Summary
It is widely accepted that drugs often bind several proteins beyond their intended target (polypharmacology), which has implications for both therapeutic efficacy and adverse-effects. Understanding of polypharmacology can lead to the exploitation of drugs in novel indications, such as the recent approval of crizotinib in ROS1-driven non small cell lung cancer[3,7,8] In this context, experimental and computational methods are increasingly being used to uncover previously unknown off-targets of drugs[3,9,10,11,12]. Of the four approved PARP inhibitors, niraparib was shown to be more selective for PARP1 and PARP2 compared to olaparib, rucaparib and talazoparib which show broader pan-PARP activity (Fig. 1b)[29] This differential intra-family PARP selectivity is insufficient to explain all the differences observed between clinical PARP inhibitors. This illustrates the limitations of any single method for identifying drug polypharmacology and indicates the need for a more comprehensive analysis[30]
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