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Avoiding Missed Opportunities by Analysing the Sensitivity of our Decisions Matthew D. Segall, Iskander Yusof, and Edmund J. Champness J. Med. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jmedchem.5b01921 • Publication Date (Web): 22 Feb 2016 Downloaded from http://pubs.acs.org on March 6, 2016
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Journal of Medicinal Chemistry is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.
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Avoiding Missed Opportunities by Analysing the Sensitivity of our Decisions Matthew D. Segall*, Iskander Yusof, Edmund J. Champness Optibrium Ltd., 7221 Cambridge Research Park, Beach Drive, Cambridge, CB25 9TL, UK
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Abstract Drug discovery is a multi-parameter optimisation process, in which the goal of a project is to identify compounds that meet multiple property criteria required to achieve a therapeutic objective. However, having chosen a profile of property criteria, their impact on the decisions made regarding progression of compounds or chemical series should be carefully considered. In some cases, the decision will be very sensitive to a specific property criterion and such a criterion may be artificially distorting the direction of the project; any uncertainty in the ‘correct’ value or the importance of this criterion may lead to valuable opportunities being missed. In this paper, we will describe a method for analysing the sensitivity of the prioritisation of compounds to a multi-parameter profile of property criteria. We show how the results can be easily interpreted and illustrate how this analysis can highlight new avenues for exploration.
Introduction Medicinal chemistry is a delicate balancing act, requiring many compound properties to be simultaneously optimised to achieve the therapeutic objectives of a drug discovery project. A safe and efficacious drug that reaches the market must have sufficient potency against its therapeutic target(s), appropriate absorption, distribution, metabolism and excretion (ADME) properties and avoid interactions with off-targets or non-specific toxicities at therapeutic concentrations to achieve an acceptable safety window. In order to guide this multi-parameter optimisation (MPO) process, it is common to define a set of property criteria or a ‘target profile’ that compounds must achieve for progression at different stages, e.g. hit selection, hit-to-lead and candidate selection. These are often defined by a multi-disciplinary project team, based on the experience of medicinal chemistry, pharmacology, drug metabolism and pharmacokinetics (DMPK) and toxicology team members, sometimes supported by statistical analysis of other compounds previously progressed for similar objectives 1, 2.
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However, having chosen a profile of property criteria, we should consider the impact that this choice will have on the decisions made, i.e. the compounds or chemical series chosen for progression. In some cases, the choice of compounds will be very sensitive to a specific property criterion or the importance given to it. In these cases, that criterion may be artificially distorting the direction of the project; if we are not very confident that the ‘right’ criterion has been used, this may lead to valuable opportunities being missed. As an analogy, consider another decision we may make in our everyday lives; choosing the ‘perfect’ hotel for a well-deserved vacation. We might look on Trip Advisor and consider many criteria including location, reviews of characteristics such as cleanliness of accommodation, proximity to an attraction, family friendliness and others. Maybe we decide that the hotel must be within half a mile of the beach and, to narrow down the choices, we filter out all of the hotels that are further away. But, what if an otherwise perfect hotel was 900 yards from the beach? This arbitrary decision may mean that we miss the vacation of a lifetime! In the context of drug discovery, consider a progression criterion for potency that specifies that the IC50 must be less than 10nM. But, what if the most active member of a chemical series with good ADME and safety characteristics has a potency of 50 nM; would it make sense to reject this series? In a simple case such as this, it may be possible to spot this exception, but with the increasing complexity and diversity of the data used in early drug discovery, these sensitivities may not always be apparent. In addition, as time progresses it can be difficult to remember the details of chemical series explored earlier in a project and consideration of these sensitivities can reveal alternative directions or backup series, should a project reach an insurmountable issue with its primary series. In another scenario, it is not uncommon to have disagreements within a project team regarding the most appropriate property criteria and their importance to the overall decision. For example, one member of a team may insist that the aqueous solubility of a compound must be at least 100 µM to consider for progression, while another may feel that this is too strict, suggesting that any downstream solubility issues could be addressed by appropriate formulation. In this scenario, the question is, “Does it matter?” If both
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criteria lead the project to select the same compounds, then this debate is not relevant to the decision being made. On the other hand, if the choice of solubility criterion will dramatically affect the direction of the project, the debate must be had in earnest. Alternatively, it may be a good strategy to sample some of the compounds selected by each criterion for progression and gather some downstream data before making a final decision about which criterion is the most appropriate. Answers to questions such as these can be addressed by sensitivity analysis, to rigorously consider the impact of small changes in property criteria and their importance in the context of a multi-parameter profile. In the paper, we will describe a method for sensitivity analysis that generates easily interpretable results, to help to guide critical compound selection decisions in drug discovery, avoiding missed opportunities and revealing potentially valuable new avenues for exploration. In the next section we will give a brief overview of approaches to multi-parameter optimisation as a foundation for a description of methods for sensitivity analysis. We will then discuss the application of these methods using some illustrative applications before drawing conclusions.
Methods Introduction to Multi-Parameter Optimisation Methods for prioritising solutions (compounds, in the context of drug discovery) are commonly described using a range of terms including MPO, multi-objective optimisation (MOOP), and multi-criteria decisionmaking (MCDM). For simplicity, we will refer to these as MPO. Comprehensive reviews of MPO methods applied in drug discovery can be found in references 3, 4. Probably the simplest approach for MPO is the use of filters, removing compounds that do not meet each of a series of property criteria in the hope that a ‘perfect’ compound will emerge that passes all of the criteria. However, particularly early on in a drug discovery project, it is unlikely that any compound under consideration will meet all of the criteria and, in practice, it is usually necessary to compromise in order to
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make progress. In such a scenario, the importance or weight of each criterion should be considered. Furthermore, filters are often unnecessarily harsh, making inappropriate distinctions between compounds. For example, a popular selection criterion based on molecular weight (MW) is that a compound should be less than 500 Da to be ‘drug like’. However, does a compound with a MW of 499 Da really have a better chance of success than another compound with MW of 501 Da? The correlation between MW and the success of compounds does not support this distinction 5. This problem is compounded by the fact that the data with which decisions are made often have significant uncertainties due to experimental variability or statistical error 6. For example, the standard deviation in a measured IC50 may be a factor of 3 (0.5 log units), so in this case a compound with a measured IC50 of 125 nM has, approximately, a 40% chance of meeting a criterion of
