1、1CONFIDENTIALMedicinal chemistry meets systems biology John Harris, cjh Consultants(Founder and consultant to BioFocus)“Cutting Edge Approaches to Drug Design”MGMS, March 2009School of Oriental and African Studies, University of London2CONFIDENTIALWhy should drug discoverers bother about biological
2、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 thera
3、peutic effect through a network of effectors the output of the network determines the drug profile (i.e. its good points and its bad points) 3CONFIDENTIALHow should they respond to the challenges of biological networks? 1970-1990 clinical success driven by selectivity for single targets (e.g. h2 ant
4、agonists, 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
5、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 r
6、esponses primary cell screening begins to gain ground.4CONFIDENTIAL 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,
7、 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, on
8、e of the earliest clinically-successful anticancer kinase inhibitors, Sutent, has been shown to be one of the least selective across the kinome5CONFIDENTIALSystems Biology and Network Pharmacology are now very well established BIOLOGICAL activities in academia and, increasingly, in pharma and biotec
9、h. 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, u
10、nfortunately, 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 mathe
11、matical models“ Whither “systems medchem”?6CONFIDENTIALHow 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”
12、 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 mo
13、dern 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?7CONFIDENTIALKinases show the way forward? Clinically effective first generation onc
14、ology 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 di
15、scovery 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 activities8CONFIDENTIALIt can be done! Lapatanib designed to hit EGFR
16、and ErbB2 in order to cover a wider range of tumour types (see 2005Drugs of the Future1225)9CONFIDENTIALTarget Discovery Approaches In silico predict therapeutically useful combination of targets by network modelling and simulation correlate with known drug profiles, protein interaction fingerprints
17、, 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 “
18、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 probes1
19、0CONFIDENTIALFesik et al. 2006Oncogene1340Akt-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 enc
20、oding CK3g1 and IMPK (inositol polyphosphate multikinase)11CONFIDENTIALLead Discovery ApproachesIn 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 overlappin
21、g pharmacophores a better betA rational approach to MKIs is possible: a) Feasibility assessmentb) Focused screening library and fragment cross-screening12CONFIDENTIALLigand SAR and cross-family common site sequencesRecent evidence supports the view that, within protein families that have a common si
22、te 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
23、 13CONFIDENTIALFeasibility assessment A dual inhibitor of LimK1 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 SAR19CONFIDENTIALAddressing multi-targeti
24、ng in a rational way using designed fragment libraries such as ThemePair FragmentsPrimary kinase targetScaffold X, sidechains a-gSecondary kinase targetScaffold X, sidechains e-hExclusion kinase targetScaffold X,Sidechains a,c,j Therefore, profitable SAR area for selective multi-targeted inhibitor i
25、s scaffold X combined with sidechains e,f and gIllustrative simplistic scenario20CONFIDENTIALPrimary targetSecondary targetExclusion targetIn 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 “m
26、enu” of scaffold and side-chain/monomer types 21CONFIDENTIALAn 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
27、in SciFinder or by commercial supplier 22CONFIDENTIALLead 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 wel
28、l 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 dif
29、ferent 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 i
30、n lead optimisation. 23CONFIDENTIALFacilitating parallel lead optimisationParallel 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. B
31、iochemical 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 bioche
32、mical 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 24CONFIDENTIALIn OncologyMKIs will beco
33、me the norm in the kinase inhibitor field; combination therapy and MTDs with kinase and synergising non-kinase drugs will emergeIn InflammationCertain 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 f
34、or autoimmune diseases and there are unpublished data for related approaches Whither other complex multifaceted diseases?25CONFIDENTIALAdditional references1) “Network pharmacology: the next paradigm in drug discovery”, A L Hopkins, Nature Chemical Biology, 2008, 682.2) “What does Systems Biology me
35、an for drug discovery” A Schrattenholz, Vukic Soskic, Current Medicinal Chemistry, 2008, 1520.3) “Designed Multiple Ligands. An emerging drug discovery paradigm” Richard Morphy, Zoran Rankovic, J Med. Chem., 2005, 6523.4) “The physicochemical challenges of designing multiple ligands” Richard Morphy,
36、 Zoran Rankovic, J Med. Chem., 2006, 4961.5) “Logic models of pathway biology”, Steven Watterson, Stephen Marshall, Peter Ghazal, Drug Discovery Today, 2008, 447.6) “Can we rationally design promiscuous drugs” A L Hopkins, J S Mason, J Overington, Current Opinion in Structural Biology, 2006, 127.7)
37、“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, 7882.8) “Selectively Nonselective Kinase Inhibition: Striking the Right Balance” R Morphy, J Med Chem.,.2010, 1413.26CONFIDENTIALAcknowledgements 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)
