A Machine Learning Approach Predicts Tissue-Specific Drug Adverse 2 Events 3 4 Neel S

bioRxiv preprint doi: https://doi.org/10.1101/288332; this version posted March 24, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 A Machine Learning Approach Predicts Tissue-Specific Drug Adverse 2 Events 3 4 Neel S. Madhukar*1,2,3,4, Kaitlyn Gayvert*1,2,3,4, Coryandar Gilvary1,2,3,4, Olivier Elemento1,2,3,4 5 6 1 HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Dept. of 7 Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA; 8 2 Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 9 10065, USA; 10 3 Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10065, USA; 11 4 Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY 10065, USA; 12 13 * co-first authors 14 15 Correspondence: Olivier Elemento ([email protected]) 16 17 ABSTRACT 18 19 One of the main causes for failure in the drug development pipeline or withdrawal post 20 approval is the unexpected occurrence of severe drug adverse events. Even though such 21 events should be detected by in vitro, in vivo, and human trials, they continue to 22 unexpectedly arise at different stages of drug development causing costly clinical trial 23 failures and market withdrawal. Inspired by the “moneyball” approach used in baseball to 24 integrate diverse features to predict player success, we hypothesized that a similar 25 approach could leverage existing adverse event and tissue-specific toxicity data to learn 26 how to predict adverse events. We introduce MAESTER, a data-driven machine learning 27 approach that integrates information on a compound’s structure, targets, and phenotypic 28 effects with tissue-wide genomic profiling and our toxic target database to predict the 29 probability of a compound presenting with different types of tissue-specific adverse 30 events. When tested on 6 different types of adverse events MAESTER maintains a high 31 accuracy, sensitivity, and specificity across both the training data and new test sets. 32 Additionally, MAESTER scores could flag a number of drugs that were approved, but later 33 withdrawn due to unknown adverse events - highlighting its potential to identify events 34 missed by traditional methods. MAESTER can also be used to identify toxic targets for 35 each tissue type. Overall MAESTER provides a broadly applicable framework to identify 36 toxic targets and predict specific adverse events and can accelerate the drug 37 development pipeline and drive the design of new safer compounds. 38 39 INTRODUCTION 40 41 Drug adverse events are currently one of the main causes of failure in drug development 42 and are one of the top 10 causes of death in the developed world1, 2. Toxicity issues 43 remain a leading cause for the rising clinical trial attrition rates3, 4. Even after a drug has 44 been approved, adverse drug reactions remain a large burden on the medical system 45 with the costs amounting to as much as $30 billion dollars annually in the USA5. 46 Furthermore the identification of the serious adverse events associated with drugs bioRxiv preprint doi: https://doi.org/10.1101/288332; this version posted March 24, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 47 frequently does not occur until after FDA approval, with as many as 50% of adverse 48 events going undetected during human trials6 . Due to the prevalence and impact of this 49 problem, the U.S. Food and Drug Administration (FDA) has established the US FDA 50 Adverse Event Reporting System (FAERS). 51 52 Most adverse event detection experiments are carried out in pre-clinical phases based 53 on animal results or during early clinical trials. However not all adverse events are 54 detected, due to several factors including limited relevance of animal models to human 55 physiology, limited sample sizes during trials, and patient populations that may not be 56 representative of the overall population5. Further complications may include the low 57 frequency or late onset of some adverse events5. As a result, retrospective studies are 58 currently an important method for further characterization of the side effects associated 59 with drugs. However this requires a large number of patients to be treated first and is 60 dependent on voluntary reporting, which is especially problematic as only 10% of all drug 61 adverse events are reported post-approval7. 62 63 Ideally possible adverse events would be detected during the pre-clinical phases of drug 64 development, even before animal studies. Cell lines and reporter assays may help detect 65 unwanted side effects early, but are often imprecise. Computational screening methods 66 are also critical components of current drug development pipelines for evaluating pre- 67 clinical toxicity. In particular, drug-likeness measures, which use molecular features to 68 estimate oral bioavailability as a proxy for drug toxicity, have been widely adopted. 69 Examples of drug-likeness methods include Lipinski’s Rule of Five8 and the Quantitative 70 Estimate for Drug Likeness9. More recently machine learning based methods have been 71 proposed for predicting drug toxicity, including previous work from our group (PrOCTOR) 72 which integrates established molecular properties with target-based features to directly 73 predict broad clinical trial toxicity10. Other groups have developed diverse methods 74 focused on predicting toxicity specific to the liver11. However no method has yet been 75 developed with the granularity to predict multiple specific adverse events across different 76 tissue types, such as heart attacks or neutropenia, for a specific drug. Better methods 77 for predicting such adverse events could improve fast-fail procedures and facilitate better 78 trial design. To address this problem, we introduce MAESTER, a new machine-learning 79 platform for the prediction of tissue-specific drug adverse events. We show that for a set 80 of 6 serious adverse events MAESTER achieves unprecedented accuracy while 81 maintaining high specificity and sensitivity. Additionally we demonstrate how MAESTER 82 could have identified drug adverse events that were missed by traditional screening 83 methodologies but led to costly market withdrawal. 84 85 RESULTS 86 87 Identifying determinants of tissue-specific toxicities and adverse events 88 We first sought to identify drugs or compounds that are specifically toxic within individual 89 tissues and compare them with compounds with no reported toxicities in these tissues. 90 We focused on a set of six tissues whose corresponding AEs are correlated with clinical bioRxiv preprint doi: https://doi.org/10.1101/288332; this version posted March 24, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 91 trial failures: liver, kidney, blood, heart, lung, and pancreas (Fig.S1A). We used the 92 SIDER database of drug side effects to identify subsets of drugs that are associated with 93 tissue-specific adverse events (TSAEs) (Table 1)12. For example we identified all drugs 94 that have been associated with liver toxicities. For each tissue, we also established a 95 “safe” set of drugs for comparisons identifying any drugs not associated with those TSAEs 96 or other AEs highly correlated with fatalities in openFDA (https://open.fda.gov/) defined 97 as having a fatality frequency > 13% (Fig.1A). For each drug, we compiled structural 98 representations in the format of SMILES from DrugBank, differential gene expression 99 profiles from the Broad Institute’s Connectivity Map (CMAP)13, growth inhibition patterns 100 across the NCI60 cell lines (NCI60) from the NCI’s Developmental Therapeutics 101 Program14, and bioassay data from PubChem15. 102 103 For each tissue we then investigated how these safe and toxic drugs compare to each 104 other. For each pair of drugs, we calculated a similarity score for each of the considered 105 data types (Methods). We found that in all tissues, tissue-specific toxic drugs were most 106 structurally similar to each other (Fig.1B). Additionally, toxic drugs tended to also be most 107 similar to other toxic drugs in terms of differential gene expression profiles (Fig.1C), 108 growth inhibition screens (Fig.1D) and bioassays (Fig.1E). Interestingly we found distinct 109 patterns across the different tissue types – for instance, growth inhibition was best able 110 to separate out drugs with blood specific adverse events, whereas gene expression 111 changes had the greatest utility in the liver. These patterns could be incredibly valuable 112 for adverse event prediction as they highlight how we can model the diversity across 113 drugs with a given side effect. For example high structural similarity between a new 114 compound and compounds known to be toxic in the heart could indicate potential cardiac 115 toxicity for that new compound. Additionally high similarity between the compound- 116 induced expression changes of a new compound with expression changes of compounds 117 with known liver toxicity could suggest liver toxicity for the new compound. 118 119 We next examined how expression of a drug’s targets could be used to predict TSAEs. 120 For this analysis we integrated tissue-specific expression data measured by the GTEX 121 database.

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