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Decision Making in Medicinal Chemistry: The Power of Our Intuition Laurent Gomez*
ACS Med. Chem. Lett. Downloaded from pubs.acs.org by 185.223.165.197 on 09/11/18. For personal use only.
Gomez Consulting, San Diego, California 92129, United States ABSTRACT: Medicinal chemists rely on their intuition to make decisions regarding the course of a medicinal chemistry program. Our ability to accurately and efficiently process large data sets routinely requires that we reduce the volume of information to manageable proportions. This prioritization process, however, can be affected by intuitive biases. One such situation is structure−activity relationship (SAR) analysis in nonadditive data sets in which attempts to intuitively predict the activity of compounds based on preliminary data can lead to erroneous conclusions. Matrix analysis can be a useful tool to accurately determine the nature of the SAR and to improve our decision-making process during an analoging campaign.
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emotional tendency to minimize loss over gain (loss aversion).4 Further analysis of the data from these two studies reveals additional insights into the chemist’s mind and their intuitive biases. It appears that the observed low consensus among chemists was not related to the number of compounds reviewed (28% for 250 compounds versus 23% for 2000 compounds reviewed). Additionally, work experience did not appear to be a factor since chemists with more than 20 years’ experience were still only 20% consistent among themselves. Among the different parameters available for consideration during their selection process (such as shape, polarity, size, lipophilicity, and IP of fragments), chemists greatly reduced the complexity by focusing only on one or two parameters. When chemists used the same parameters during their selection process, the results show that they did not necessarily agree on the optimal and/or desirable values. The observed disparity among chemists may be related to the anchoring effect as characterized by psychologists Tversky and Kahneman. Their research showed that decisions are affected by a reference point that greatly influences subsequent judgments about values.5 In situations of uncertainty, when asked to make decisions or provide estimates, people start from an initial value (reference point or anchor) and adjust it to reach their final decision. However, different reference points yield different estimates that are usually biased toward the initial values. Tversky and Kahneman conducted several studies that showed exposure to unrelated information prior to conducting a specific task has a remarkable influence on the ultimate outcome. Their findings illustrate the susceptibility of our cognitive systems to the biasing influence of anchors. It is therefore conceivable that the low consensus among chemists is the result, in part, of different reference values that they have established based on their unique experience, background, and training. Worth noting is that the low consensus observed
edicinal chemistry is an interdisciplinary process that includes chemistry (organic and computational), pharmacology, and DMPK working together toward the common goal of drug discovery. The success of any medicinal chemistry program relies not only on the quality of the data that the scientists from these disciplines generate but also the quality of the decisions that are made from the available data. Consequently, the ability of any medicinal chemist to rapidly and accurately make decisions becomes a critical factor in the ultimate success of the program. Scientists routinely review large and complex data sets including, for example, calculated properties, potency, selectivity, ADME, structural biology, and computational data. This increased complexity in both volume and diversity of information that needs to be processed renders this task even more challenging. To increase efficiency of this data-review process, human cognition tends to reduce the amount of information to a manageable proportion.1 This prioritization process appears to be based, in part, on intuitive factors and can be subject to cognitive biases. Two studies have attempted to quantify the reliability and consistency of this prioritization process by asking medicinal chemists to review small and large data sets of compounds.2,3 The task was designed such that chemists were evaluated on many factors including (1) consistency against themselves after reviewing the same list of compounds twice and (2) consistency among their chemistry colleagues. The results showed that medicinal chemists agree with themselves only 50% of the time, meaning that half the time chemists changed their mind about what they thought was an acceptable or unacceptable compound. More surprisingly, chemists were only 28% consistent among their colleagues. Furthermore, the studies also showed that the consistency results were dependent upon the way the question was asked. Indeed, a greater disparity in the consistency among chemists was observed when compounds were characterized as good versus bad. This later observation may be associated with our © XXXX American Chemical Society
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DOI: 10.1021/acsmedchemlett.8b00359 ACS Med. Chem. Lett. XXXX, XXX, XXX−XXX
ACS Medicinal Chemistry Letters
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relatively small. However, the study showed that the combination of two large R1 and R2 substituents led to a significant decrease in potency (up to 2−3 log units, Figure 2).
among chemists should not necessarily be alarming. In most situations, this heterogeneity creates an optimal team of scientists with different ideas on how to achieve a common objective. In a collaborative environment, they can leverage their differences and thrive to success (checks and balances). However, these cognitive biases may negatively affect the course of a medicinal chemistry program during hit selection, structure−activity relationship (SAR) analysis, lead optimization, selection of biological studies, and candidate selection. One such example is SAR analysis biases, illustrated by the case study in Figure 1. In this 3D bar graph, both x and y axes
Figure 1. Matrix analysis of nonadditive SAR. 1
Figure 2. Double transformation cycle of nonadditive SAR.
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represent substitutions on Ar and Ar , while the z axis represents the activity plotted as the pKi. Initial analysis of an almost complete set of a 3 × 3 matrix (Plot 1) seems to indicate that Ar2 = 3b is consistently more potent than the other two substituents (Ar2 = 1b or 2b) regardless of the nature of Ar1. Intuitively, one might make the decision to further focus on the Ar2 = 3b for further analoging and discard Ar2 = 1b, which does not appear to be very promising. Surprisingly, the full data set (Plot 2) shows a completely different SAR trend where Ar2 = 1b combined with Ar1 = 3a constitutes the optimal combination leading to the most potent compound of the matrix. This real case example6,7 illustrates a misleading assumption that activity can be described as an additive relationship in which ligand activity = activity of the core + sum of activity Ar1 and Ar2. Instead, ligand activity = activity of the core + sum of activity Ar1 and Ar2+ an interaction term, which accounts for changes in binding mode or intramolecular interactions between the substituents. This example is a case of nonadditive SAR where no assumptions can be made with regards to intuitively predicting the activity of compounds based on initial values (anchoring bias). In another study, a rare case of nonadditive SAR was reported in which the interaction term was determined to be associated with local protein structural changes upon ligand binding.8 A 6 × 5 matrix was designed to evaluate the influence of the size of two distinct substituents on activity. Using the double transformation mathematical equation described by Kramer,9 the nonadditive behavior of the entire combinatorial matrix was unambiguously quantified. It is worth noting that a SAR analysis should always include a careful determination of the experimental variability of the biological assay since this variability can lead to a significant amount of apparent nonadditivity. In this most recent example, the nonadditive values were determined to be well above the mean standard error of the assay. The results indicated that a small R1 substituent combined with a large R2 was tolerated. Similarly, a large R1 was tolerated by the enzyme if R2 was kept
The use of crystallographic methods demonstrated that the source of nonadditivity originated from a protein conformational change associated with a single amino-acid residue. Upon ligand binding to the protein, an induced lipophilic pocket was formed, which reduced the size of an adjacent subpocket contributing to the strong nonadditive SAR. This assumption of additivity originates presumably from our fundamental training in chemistry where several relationships have successfully been described as such. Indeed, thermodynamics equations describing enthalpy and entropy, for example, adhere to the rules of additivity. Similarly, cLogP10 and tPSA11 have successfully been described as additive relationships (the sum of atomic contributions). Unfortunately, ligand−protein interactions are generally not additive, and although medicinal chemists are aware of this phenomenon, the axiom is not always taken into consideration. In situations where two variables (R1 and R2) need to be assessed as part of the SAR campaign, medicinal chemists commonly adopt a monodimensional approach in which one substituent is kept constant (R1 for instance) and combined with several differently substituted R2 groups. After testing the compounds, the next step typically consists in selecting a small subset of R2 substitutions, based on promising data, and combining them with a set of R1. The merit of this approach resides in the fact that a smaller set of compounds are synthesized with the expectation that the optimal combination of R1 and R2 substitutions will be identified. In most cases, medicinal chemists assume additivity during this monodimensional analoging campaign without conducting a matrix data analysis to confirm it. The potential risk of such assumptions arises when the SAR is nonadditive, and specific substitutions are discarded too early in the campaign. Computational tools are available to conduct SAR analyses allowing medicinal chemists to determine and study the nature of the SAR. Conducting a matrix analysis does not imply that a matrix synthesis needs to be implemented in the case of nonadditive B
DOI: 10.1021/acsmedchemlett.8b00359 ACS Med. Chem. Lett. XXXX, XXX, XXX−XXX
ACS Medicinal Chemistry Letters
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(11) Ertl, P.; Rohde, B.; Selzer, P. Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. J. Med. Chem. 2000, 43, 3714−3717.
SAR since the chemistry might not be easy to execute. Rather, such analysis will raise awareness about potential biases and provide additional insights into ligand−protein interactions. With the ever-increasing volume of data generated and the importance of accurately and efficiently reviewing this most valuable project resource, machine learning coupled with matrix analysis can represent a valuable tool to the medicinal chemist. It should be noted that despite potential pitfalls, human intuition is an intrinsic part of any decision-making process and remains a key component to the success of medicinal chemistry programs. Raising awareness about our intuitive biases and imperfection in decision making is an important step toward maximizing our chance of success.
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AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. ORCID
Laurent Gomez: 0000-0002-9172-7840 Notes
Views expressed in this editorial are those of the author and not necessarily the views of the ACS. The author declares no competing financial interest.
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ACKNOWLEDGMENTS The author thanks Leslie, Aiden, and Elise Gomez for their helpful contributions to the graphics. REFERENCES
(1) Fan, J. An information theory account of cognitive control. Front. Hum. Neurosci. 2014, 8, 680. (2) Kutchukian, P. S.; Vasilyeva, N. Y.; Xu, J.; Lindvall, M. K.; Dillon, M. P.; Glick, M.; Coley, J. D.; Brooijmans, N. Inside the mind of a medicinal chemist: the role of human bias in compound prioritization during drug discovery. PLoS One 2012, 7, e48476. (3) Lajiness, M. S.; Maggiora, G. M.; Shanmugasundaram, V. Assessment of the consistency of medicinal chemists in reviewing sets of compounds. J. Med. Chem. 2004, 47, 4891−4896. (4) Kahneman, D.; Tversky, A. Choices, values, and frames. Am. Psychol. 1984, 4, 341−350. (5) Tversky, A.; Kahneman, D. Judgement under uncertainty: heuristics and biases. Science 1974, 185, 1124−1131. (6) McClure, K.; Hack, M.; Huang, L.; Sehon, C.; Morton, M.; Li, L.; Barrett, T. D.; Shankley, N.; Breitenbucher, J. G. Pyrazole CCK(1) receptor antagonists. Part 1: solution-phase library synthesis and determination of Free-Wilson additivity. Bioorg. Med. Chem. Lett. 2006, 16, 72−76. (7) Sehon, C.; McClure, K.; Hack, M.; Morton, M.; Gomez, L.; Li, L.; Barrett, T. D.; Shankley, N.; Breitenbucher, J. G. Pyrazole CCK1 receptor antagonists. Part 2: SAR studies by solid-phase library synthesis and determination of Free−Wilson additivity. Bioorg. Med. Chem. Lett. 2006, 16, 77−80. (8) Gomez, L.; Xu, R.; Sinko, W.; Selfridge, B.; Vernier, W.; Ly, K.; Truong, R.; Metz, M.; Marrone, T.; Sebring, K.; Yan, Y. G.; Appleton, B.; Aertgeerts, K.; Massari, M. E.; Breitenbucher, J. G. Mathematical and structural characterization of strong non-additive SAR caused by protein conformational changes. Just accepted. J. Med. Chem. 2018, DOI: 10.1021/acs.jmedchem.8b00713. (9) Kramer, C.; Fuchs, J. E.; Liedl, K. R. Strong nonadditivity as a key structure-activity relationship feature: distinguishing structural changes from assay artifacts. J. Chem. Inf. Model. 2015, 55, 483−494. (10) Mannhold, R.; Poda, G. I.; Ostermann, C.; Tetko, I. V. Calculation of molecular lipophilicity: State-of-the-art and comparison of log P methods on more than 96,000 compounds. J. Pharm. Sci. 2009, 98, 861−893. C
DOI: 10.1021/acsmedchemlett.8b00359 ACS Med. Chem. Lett. XXXX, XXX, XXX−XXX
