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Medicinal Chemistry meets Systems Biology

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Medicinal Chemistry meets Systems Biology

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    Medicinal chemistry meets systems biology John Harris, cjh Consultants (Founder and consultant to BioFocus) Cutting Edge Approaches to Drug Design MGMS, March 2009 School of Oriental and African Studies, University of London Why should drug discoverers bother about biological networks? nearly all drugs can hit more than one effector target in an organism not all non-target effectors are off-targets, metabolic systems or transporters accumulated genomic/proteomic/analytical pharmacological knowledge confirm that several highly efficacious drugs exert their overall therapeutic effect through a network of effectors the output of the network determines the drug profile (i.e. its good points and its bad points) How should they respond to the challenges of biological networks? 1970-1990 clinical success driven by selectivity for single targets (e.g. h2 antagonists, AII inhibitors). Medchem is driven by isolated enzyme assays or analytical pharmacology. 1990-2000 as therapeutic targets become more challenging, high-throughput screening, fed by massively combinatorial chemistry, drives expectations upwards BUT the same technology demands assay systems even less related to the constituted organism! 2000- 2005 unmet expectations drive a much more focused approach to screening but compounds are still, essentially, optimised against single reductionist assays. 2005- present increasing realisation that reductionist assays do not predict cell network responses primary cell screening begins to gain ground. most of the clinically effective antipsychotics require polypharmacological mechanisms (clozapine, a broad-spectrum biogenic amine ligand, is as effective as 5HT2a selective atypical antipsychotics such as olanzapine, ziprasidone, etc. (see Roth et al., 2004NatureRevDrugDiscovery353) in anti-infective therapy, polypharmacology is common, e.g. Wellcomes Septrin (trimethoprim and sulfamethoxazole hitting the bacterial network) or various HIV therapies (NNRTIs and protease inhibitors) Many clues along the way.. more recently, one of the earliest clinically-successful anticancer kinase inhibitors, Sutent, has been shown to be one of the least selective across the kinome Systems Biology and Network Pharmacology are now very well established BIOLOGICAL activities in academia and, increasingly, in pharma and biotech. They are driven by major technology advances in high-content cell screening, cellular disease modelling and data handling/knowledge extraction. (Sauer et al., Science (2007), 316, 550) "The reductionist approach has successfully identified most of the components and many of the interactions but, unfortunately, offers no convincing concepts or methods to understand how system properties emerge...the pluralism of causes and effects in biological networks is better addressed by observing, through quantitative measures, multiple components simultaneously and by rigorous data integration with mathematical models" Whither systems medchem? How should the medicinal chemist respond? Historically screen in a black box empirical SAR but high relevance and guaranteed efficacy Contemporary screen target in isolation precision SAR but relevance and efficacy unclear The compromise take secondary screening into the cellular context (still much scepticism about primary cellular screening!); really depends on the degree to which the cell assays reproduce the target disease So how DO we blend the efficacy lessons of the past, underpinned by network pharmacology evidence, with modern screening and secondary assay technologies? How much must we change our mindset? After all, we optimise activity and ADME/PK more or less in parallel these days is an extra parallel target or two a quantum leap? Kinases show the way forward? Clinically effective first generation oncology drugs (e.g. Sutent, Sorafenib) act at several/multiple target kinases and mutants These earlier multiple kinase inhibitors (MKIs) were discovered serendipitously (see 2006NatureReviewsDrugDisc835) How do we discover and design MKIs rationally? (see 2010JMC1413) The challenges Multiple target discovery theoretical and analytical Lead discovery cross-screening; fragment re-assembly; chemoinformatics Lead optimisation balance of activities into the nearly-unknown balance of physicochemical properties balance of off-target activities It can be done! Lapatanib designed to hit EGFR and ErbB2 in order to cover a wider range of tumour types (see 2005Drugs of the Future1225) Target Discovery Approaches In silico predict therapeutically useful combination of targets by network modelling and simulation correlate with known drug profiles, protein interaction fingerprints, biomarker data key input from broad chemogenomic databases which correlate high-quality assay data and in vivo data (pre-clinical and clinical) with specific targets In vitro Isolated enzyme profiling is arguably too reductionist at best can only point to possible targets or pathways cell lysate fishing using ligand probes is a better indication especially if studying affinities and response time-course (e.g. Kinaxos KinAffinity, Cellzomes BioBeadsTM High-content screening in cellular disease models, tracking networks, not just specific targets Counter-screening using characterised probes Fesik et al. 2006Oncogene1340 Akt-co-operating kinases A-443654 was counterscreened against 768-O cells transfected with a kinome-wide (443 kinases + 64 orphans) siRNA library Akt-dependent apoptosis and blockade of critical Akt signalling pathway nodes were both sensitised by siRNAs encoding CK3g1 and IMPK (inositol polyphosphate multikinase) Lead Discovery Approaches In general, diversity screening against multiple targets may be even less cost-effective that against single targets, and key intra-family SAR is unlikely to be revealed. Pre-filtering based on overlapping pharmacophores a better bet A rational approach to MKIs is possible: Feasibility assessment b) Focused screening library and fragment cross-screening Ligand SAR and cross-family common site sequences Recent evidence supports the view that, within protein families that have a common site of action, similar ligands tend to bind to similar family members (see Bamborough, 2008JMC7898; Vieth, 2005DDT839) BioFocus has developed a simple roadmap based on the common geometry of the kinase ATP site (activated state) which enables quick assessment of multitarget SAR crossover feasibility Feasibility assessment A dual inhibitor of LimK1 & LimK2? SoftFocus library screening gave 3 scaffolds but only two subsite-selective fragment classes (based on homology model docking): Best dual activity for these U-shaped compounds was 12-fold (in either direction) Subsequent report showed nanomolar dual (equipotent) inhibition for a series of linear compounds of general structure: Focused library cross-screening Despite close sequences, SAR relationships for similar kinases remain unpredictable, especially where overt or more subtle differences in binding mode occur Many organisations have built up vast datasets of cross-screened kinase inhibitors which can be mined for MKI leads IPR break-out is more of a problem with this strategy though Fragment screening and cross-screening Increasingly popular approach well suited to MKI design Fragments sample greater chemical space and allow identification of preferred monomers and/or monomers which may not be picked up when screening a more decorated system Fragments have no pre-determined second vectors and are able to probe sub-sites more extensively Compounds can be grown from common scaffolds or pre-determined privileged sub-site fragments Larger fragments can be screened in biochemical assays ThemePair Fragment Libraries Small fragments: 1 component system Require affinity-based techniques (HTX-ray, NMR, SPR) ThemePair Fragment library compounds: 2 component systems lead-like compounds, good ligand efficiency, solubility; HC-biochemical screening or affinity methods Traditional focused library compounds: 3 component systems more likely to give potency in biochemical screens less likelihood/compound of multitarget SAR Addressing multi-targeting in a rational way using designed fragment libraries such as ThemePair Fragments Primary kinase target Scaffold X, sidechains a-g Secondary kinase target Scaffold X, sidechains e-h Exclusion kinase target Scaffold X, Sidechains a,c,j Therefore, profitable SAR area for selective multi-targeted inhibitor is scaffold X combined with sidechains e,f and g Illustrative simplistic scenario In reality, likely that similar scaffolds will show similar SAR at the themepair fragment level Favoured area of space for required hit profile: Can provide a menu of scaffold and side-chain/monomer types

    Slide 21:An example BioFocus library TPF11

    large library (ca. >700 compounds) based on two cores and 9 scaffolds extensively elaborated at a single position so that the scaffold becomes the effective second variable. These compounds are not reported in SciFinder or by commercial supplier

    Lead optimisation where (medchem) going gets tough! Balance of activities into the nearly-unknown; until more data are available from network biomarker and enzyme-occupancy studies, balanced potency is the best guess very high multipotency may well not be required Balance of physicochemical properties tricky for MKIs where structural additivity tends to correlate with selectivity however, deliberate choice of overlapping pharmacophores helps; non-oncology applications are more challenging Balance of off-target activities this issue is no different in principle to that for so-called selective kinase inhibitors, of which there are not many. Isolated enzyme assays are, at best, an approximate guide to undesirable intra-family activities. Monitoring cellular target/s activity against in vitro and in vivo toxicity readouts are essential in lead optimisation. Facilitating parallel lead optimisation Parallel optimisation is the ideal: This is the area of greatest current medchem caution! Lead optimisation against more than one non-ADMET/PK target is somewhat foreign to current practice, at least outside the kinase area. Biochemical and cellular kinase assays need to be run in close conjunction with each other, even more so than for monovalent kinase inhibitors HCS technologies are beginning to impact optimisation in this way. Cellular assays can also measure inhibitory mechanisms which are missed by current biochemical methods Cross-target SARs are, by their nature, more complex than single-target SARs and compromises are generally to be expected Therefore it is very important to qualify these SAR compromises, preferably in cellular disease models or even primary cells In Oncology MKIs will become the norm in the kinase inhibitor field; combination therapy and MTDs with kinase and synergising non-kinase drugs will emerge In Inflammation Certain MKIs will make it to clinic and safety assessments will be very interesting. For example, Palau have DD-2, a dual Jak3/Syk inhibitor in preclinical for autoimmune diseases and there are unpublished data for related approaches Whither other complex multifaceted diseases? Additional references Network pharmacology: the next paradigm in drug discovery, A L Hopkins, Nature Chemical Biology, 2008, 682. What does Systems Biology mean for drug discovery A Schrattenholz, Vukic Soskic, Current Medicinal Chemistry, 2008, 1520. Designed Multiple Ligands. An emerging drug discovery paradigm Richard Morphy, Zoran Rankovic, J Med. Chem., 2005, 6523. The physicochemical challenges of designing multiple ligands Richard Morphy, Zoran Rankovic, J Med. Chem., 2006, 4961. Logic models of pathway biology, Steven Watterson, Stephen Marshall, Peter Ghazal, Drug Discovery Today, 2008, 447. Can we rationally design promiscuous drugs A L Hopkins, J S Mason, J Overington, Current Opinion in Structural Biology, 2006, 127. Discovery of multitarget inhibitors by combining molecular docking with common pharmacophore features D Wei, X Jiang, L Zhou, J Chen, Z Chen, C He, K Yang, Y Liu, J Pei, L Lai, J Med. Chem., 2008, 7882. Selectively Nonselective Kinase Inhibition: Striking the Right Balance R Morphy, J Med Chem.,.2010, 1413. Acknowledgements for helpful discussions: Richard Morphy (Schering-Plough) Kate Hilyard, Chris Newton (BioFocus) Ian James (Almac Biosciences) John Overington (EMBL Cambridge) Colin Telfer, Finbarr Murphy (Lee Oncology)
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