The human genome encodes 20,000 protein-coding genes, with about 10 percent of these being small molecule druggable.1 Dysregulation of a specific protein can lead to a specific disease and its normalisation is sought by targeting it with a suitable drug. In most cases, drugs lack absolute specificity and act upon the targets they were designed against as well as unrelated proteins. This can lead to unanticipated detrimental consequences in the clinic, namely poor safety profiles and lack of efficacy. As a case study to illustrate this, the BCR-ABL tyrosine kinase inhibitor Imatinib (250 nM IC50), approved as a therapy for chronic myeloid leukaemia, has been shown to be effective at relatively high doses (400-600 mg/day) yet is unfortunately associated with frequent adverse events (mainly mild or moderate in severity; serious adverse events included fluid retention, cytopenia and hepatotoxicity). The second- and third-generation and more potent BCR-ABL tyrosine kinase inhibitors, namely Dasatinib (3 nM IC50) and Bosutinib (1 nM IC50), have improved potency against the primary BCR-ABL tyrosine kinase target, but are also known to have additional liabilities in the nM potency range against other kinases including BTK, LYN and SRC. This is unsurprising given that kinases often act in networks and pathways which can lead to drug resistance.2 Secondly, in the case of panobinostat, an approved histone deacetylase (HDAC) inhibitor, adverse clinical observations in the form of tyrosinaemia have been reported. A chemo-proteomics study revealed that panobinostat also targets phenylalanine hydroxylase leading to an increase in phenylalanine and decrease in tyrosine levels and thus the adverse clinical observation.3 Finally, comprehensive profiling of HDAC inhibitors unexpectedly revealed that the metallo-β-lactamase domain-containing protein 2 (MBLAC2) is a frequent off-target of HADC inhibitors.4
The above case studies provide evidence that the detailed profiling of compounds as early as possible will de-risk them in the drug discovery value chain and allow for the early identification of potential issues in terms of safety and efficacy prior to clinical trials. This can be achieved by profiling compounds in the widest possible range of in vitro assays using multiplex spatial and temporal imaging, which can dissect disease pathophysiology, providing insights into their mechanism of action, off-target and resistance mechanisms. This approach also has the potential to be used in a patient-driven approach to bridge basic research, translational research, drug discovery, clinical research, patient stratification and identification of optimised treatment regimens. This approach has historically been performed to a limited extent using multiplex high content imaging,5 and can now be further adapted to allow for multiplex spatial and temporal imaging6 in combination with omics approaches. It is expected that the underlying causes of poor safety and lack of efficacy, often due to compound poly-pharmacology which can be difficult to predict, will emerge when using the multiplex spatial and temporal imaging techniques in conjunction with omics methods. This approach will simultaneously capture aspects of the genome, epigenome, metabolome, proteome and transcriptome.
In addition to the above, in silico methods are being employed to progress small molecule drug discovery projects, for example by modelling and docking studies of drugs and proteins to rationalise structure-activity relationships. These methods are constantly being improved using machine learning and artificial intelligence approaches to rapidly deliver industry standard lead-like compounds, ie, with optimised chemical and pharmacological properties, pharmacokinetics, safety and toxicity profiles.
In light of the above case studies, there is justification to perform comprehensive multiplex spatial and temporal profiling of drugs as early as possible in the drug discovery value chain and allow for the early identification of potentially undesirable events prior to initiating expensive clinical trials.
Sheraz Gul is the Head of Assay Development and Drug Repurposing at the Fraunhofer Institute, Germany. He has professional experience in the field of drug discovery, assay development and screening gained while employed in academia. He has co-authored more than 80 peer-reviewed papers, book chapters and patents including the Enzyme Assays: Essential Data handbook. He also has an interest in education and provides hands-on drug discovery training in the form of workshops.
1. Hopkins AL, Groom CR. The druggable genome. Nature Reviews Drug Discovery. 2002;1(9):727–30.
2. Hantschel O, Rix U, Superti-Furga G. Target spectrum of the BCR-ABL inhibitors imatinib, nilotinib and dasatinib. Leukemia & Lymphoma. 2008;49(4):615–9.
3. Becher I, Werner T, Doce C, et al. Thermal profiling reveals phenylalanine hydroxylase as an off-target of panobinostat. Nature Chemical Biology. 2016;12(11):908–10.
4. Lechner S, Malgapo MI, Grätz C, et al. Target deconvolution of HDAC pharmacopoeia reveals MBLAC2 as common off-target. Nature Chemical Biology. 2022;18(8):812–20.
5. Lin S, Schorpp K, Rothenaigner I, Hadian K. Image-based high-content screening in Drug Discovery. Drug Discovery Today. 2020;25(8):1348–61.
6. Bray M-A, Singh S, Han H, et al. Cell painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. Nature Protocols. 2016;11(9):1757–74.