We recently used drug-induced transcriptomic responses and whole-genome sequences in healthy human induced pluripotent stem cell (iPSC)-derived cardiomyocytes to identify cellular pathways and genomic variants potentially associated with the cardiotoxic effects of tyrosine kinase inhibitors (TKIs) and anthracyclines. Here, we describe predicTox (www.predictox.org), an interactive website that organizes our data and its integration with knowledge from cell pathways and genomic databases. DrugTox summary cards give results of these analyses and metadata for each drug. Fields include cardiotoxicity risk scores curated from the FDA Adverse Event Reporting System, cell pathways, and genomic variants potentially associated with drug-induced cardiotoxicity. At a detailed level, predicTox provides a ranked list of up- and downregulated pathways that are predominantly induced by cardiotoxic TKIs as well as lists of their pathway genes and the specific cardiotoxic TKIs inducing those pathways. predicTox provides downloadable lists of drug-induced differentially expressed genes and pathways as well as drug-related genomic variants associated with cardiotoxicity. Statistical metrics are given. Mathematical models allow simulation of drug effects on heart physiology. Building on the results of our algorithm for independent reidentification of the well-known rs2229774 variant for anthracycline-induced cardiotoxicity, we describe how our data can be queried to identify potential variants associated with drug-induced cardiotoxicity by affecting a drug's pharmacodynamics and pharmacokinetics.

Medication therapy in older adults is complicated by the frequent presence of comorbidity that requires coadministration of multiple therapeutic agents. This need for polypharmacy increases the likelihood of potentially harmful drug interactions and adverse events. In addition, there are important pharmacokinetic and pharmacodynamic changes in older adults that further complicate the therapy of these individuals. Among the former are reduced renal function and CYP-mediated drug metabolism. Older patients are also more sensitive to drugs that act on the central nervous and cardiovascular systems and indiscriminate use of agents that act on these systems leads to an increase in falls and serious injury. This chapter highlights that need for more research and clinical guidance in this area.

There are many barriers to deprescribing in the routine care of older inpatients with polypharmacy. Implementation is limited by factors related to clinicians, patients, and the acute care setting. A short (11 min) e-learning module for multidisciplinary hospital clinicians was developed to address two commonly reported barriers: awareness of polypharmacy and self-efficacy in deprescribing.1) Describe the level of awareness of polypharmacy and self-efficacy of deprescribing in multi-disciplinary hospital clinicians following completion of an online e-learning module; and 2) describe the immediate impact of an online educational module in awareness and self-efficacy of polypharmacy and deprescribing in senior medical students.A questionnaire was developed and administered to hospital clinicians following completion of the e-learning module. Senior medical students undertook the questionnaire pre- and post-module.Overall, 99 hospital clinicians with diverse clinical roles, experience, and ages, and 30 medical students completed the questionnaire. Although most (≥80%) hospital clinicians reported a general awareness of polypharmacy and deprescribing, there was moderate to low current activity in medication review and deprescribing, a perceived lack of role in medication review by junior doctors, and minimal knowledge of deprescribing tools. Use of a previously validated self-efficacy questionnaire showed lowest self-efficacy in domains related to developing deprescribing plans and implementing them. Pre-post analysis of medical student responses found a small statistically significant improvement following viewing the module in awareness of polypharmacy, deprescribing and deprescribing tools, perception of their role in deprescribing, and self-efficacy in planning and implementation of deprescribing decisions.Hospital clinicians and senior medical students had limited self-efficacy in deprescribing and hospital clinicians reported they did not deprescribe frequently. Targets for educational and behavioral interventions were identified. A short e-learning module on polypharmacy and deprescribing may be a useful component of a multi-strategic intervention to implement deprescribing into routine inpatient care.

Background Adverse outcomes associated with advanced diseases are often exacerbated by polypharmacy. Objectives The current study investigated an association between exposure to anticholinergic and sedative medicines and falls in community-dwelling older people, after controlling for potential confounders. Methods We conducted a retrospective cross-sectional study of a continuously recruited national cohort of community-dwelling New Zealanders aged 65 years and over. Participants had an International Resident Assessment Instrument-Home Care (interRAI-HC) assessment between 1 September 2012 and 31 January 2016. InterRAI-HC is a comprehensive, multi-domain, standardised assessment. This study captured 18 variables, including fall frequency, from the interRAI. These data were deterministically matched with the Drug Burden Index (DBI) for each participant, derived from an anonymised national dispensed pharmaceuticals database. DBI groupings were statistically ascertained, and ordinal regression models employed. Results Overall, there were 71,856 participants, with a mean age of 82.7 years (range 65-106); 43,802 (61.0%) were female, and 63,578 (88.5%) were New Zealand European. In unadjusted and adjusted analyses, DBI groupings were related to falls (p<0.001). A DBI score >3 was associated with a 41% increase in falls compared with a DBI score of 0 (p < 0.001). There was a 'dose-response' relationship between DBI levels and falls risk. Conclusions DBI was found to be independently and positively associated with a greater risk of falls in this cohort after adjustment for 18 known confounders. We suggest that the DBI could be a valuable tool for clinicians to use alongside electronic prescribing to help reduce falls in older people.

ABSTRACT Background: β2‐adrenoreceptors have recently been identified as regulators of the α‐synuclein gene, which is implicated in the pathogenesis of Parkinson's disease. Objective: The objectives of this study were to assess the association between use of β2‐agonists and β‐antagonists and the risk of developing PD. Methods: We conducted a nested case‐control study in a cohort of 1,762,164 adults without a diagnosis of PD. They were identified on January, 1, 2004, from the electronic medical records of the largest health care provider in Israel. Participants were followed up until June 30, 2017, for the occurrence of PD. Ten randomly selected controls were matched to each case of PD on age, sex, ethnic group, and duration of follow‐up. Results: During follow‐up 11,314 patients were newly diagnosed with PD and were matched with 113,140 controls. An increased risk of PD was seen with the use of nonselective β‐antagonists (RR, 2.04 [1.90‐2.20]) but not with the use of selective β1‐antagonists (RR, 1.00 [0.95‐1.05]). Use of β2‐agonists was associated with reduced risk of PD (RR, 0.89 [0.82‐0.96] for short‐acting; RR, 0.84 [0.76‐0.93] for long‐acting; and RR, 0.49 [0.25‐0.92] for ultra‐long‐acting β2‐agonists). In an analysis of individual drugs, propranolol and salbutamol were significantly associated with PD risk, even when these drugs were ascertained 5 years prior to the index date, compared with nonusers (RR, 1.31 [1.08‐1.58] and 1.89 {1.53‐2.33]) in patients who filled <6 and ≥6 propranolol prescriptions, respectively; the corresponding RRs for salbutamol were 0.95 (0.83‐1.08) and 0.65 (0.45‐0.94), respectively. Conclusions: Use of propranolol appears to be associated with an increased risk of PD, whereas use of β2‐agonists is associated with a decreased risk of PD. © 2018 International Parkinson and Movement Disorder Society

OBJECTIVESTo identify the top priority areas for research to optimize pharmacotherapy in older adults with cardiovascular disease (CVD).DESIGNConsensus meeting.SETTINGMultidisciplinary workshop supported by the National Institute on Aging, the American College of Cardiology, and the American Geriatrics Society, February 6–7, 2017.PARTICIPANTSLeaders in the Cardiology and Geriatrics communities, (officers in professional societies, journal editors, clinical trialists, Division chiefs), representatives from the NIA; National Heart, Lung, and Blood Institute; Food and Drug Administration; Centers for Medicare and Medicaid Services, Alliance for Academic Internal Medicine, Patient‐Centered Outcomes Research Institute, Agency for Healthcare Research and Quality, pharmaceutical industry, and trainees and early career faculty with interests in geriatric cardiology.MEASUREMENTSSummary of workshop proceedings and recommendations.RESULTSTo better align older adults’ healthcare preferences with their care, research is needed to improve skills in patient engagement and communication. Similarly, to coordinate and meet the needs of older adults with multiple comorbidities encountering multiple healthcare providers and systems, systems and disciplines must be integrated. The lack of data from efficacy trials of CVD medications relevant to the majority of older adults creates uncertainty in determining the risks and benefits of many CVD therapies; thus, developing evidence‐based guidelines for older adults with CVD is a top research priority. Polypharmacy and medication nonadherence lead to poor outcomes in older people, making research on appropriate prescribing and deprescribing to reduce polypharmacy and methods to improve adherence to beneficial therapies a priority.CONCLUSIONThe needs and circumstances of older adults with CVD differ from those that the current medical system has been designed to meet. Optimizing pharmacotherapy in older adults will require new data from traditional and pragmatic research to determine optimal CVD therapy, reduce polypharmacy, increase adherence, and meet person‐centered goals. Better integration of the multiple systems and disciplines involved in the care of older adults will be essential to implement and disseminate best practices. J Am Geriatr Soc 67:371–380, 2019.

BACKGROUND The Drug Burden Index (DBI) calculates the total sedative and anticholinergic load of prescribed medications and is associated with functional decline and hip fractures in older adults. However, it is unknown if confounding factors influence the relationship between the DBI and hip fractures. The objective of this study was to evaluate the association between the DBI and hip fractures, after correcting for mortality and multiple potential confounding factors. METHODS A competing-risks regression analysis conducted on a prospectively recruited New Zealand community-dwelling older population who had a standardized (International Resident Assessment Instrument) assessment between September 1, 2012, and October 31, 2015, the study's end date. Outcome measures were survival status and hip fracture, with time-varying DBI exposure derived from 90-day time intervals. The multivariable competing-risks regression model was adjusted for a large number of medical comorbidities and activities of daily living. RESULTS Among 70,553 adults assessed, 2,249 (3.2%) experienced at least one hip fracture, 20,194 (28.6%) died without experiencing a fracture, and 48,110 (68.2%) survived without a fracture. The mean follow-up time was 14.9 months (range: 1 day, 37.9 months). The overall DBI distribution was highly skewed, with median time-varying DBI exposure ranging from 0.93 (Q1 = 0.0, Q3 = 1.84) to 0.96 (Q1 = 0.0, Q3 = 1.90). DBI was significantly related to fracture incidence in unadjusted (p < .001) and adjusted (p < .001) analyses. The estimated subhazard ratio was 1.52 (95% confidence interval: 1.28-1.81) for those with DBI > 3 compared with those with DBI = 0 in the adjusted analysis. CONCLUSIONS In this study, increasing DBI was associated with a higher likelihood of fractures after accounting for the competing risk of mortality and adjusting for confounders. The results of this unique study are important in validating the DBI as a guide for medication management and it could help reduce the risk of hip fractures in older adults.

Drug-induced toxicity is a major public health concern that leads to patient morbidity and mortality. To address this problem, the Food and Drug Administration is working on the PredicTox initiative, a pilot research program on tyrosine kinase inhibitors, to build mechanistic and predictive models for drug-induced toxicity. This program involves integrating data acquired during preclinical studies and clinical trials within pharmaceutical company development programs that they have agreed to put in the public domain and in publicly available biological, pharmacological, and chemical databases. The integration process is accommodated by biomedical ontologies, a set of standardized vocabularies that define terms and logical relationships between them in each vocabulary. We describe a few programs that have used ontologies to address biomedical questions. The PredicTox effort is leveraging the experience gathered from these early initiatives to develop an infrastructure that allows evaluation of the hypothesis that having a mechanistic understanding underlying adverse drug reactions will improve the capacity to understand drug-induced clinical adverse drug reactions.

About 50 years ago, Harry Shirkey MD, a pediatrician coined the term that infants and children are “therapeutic or pharmaceutical orphans.”1 This is because many of the drugs approved in the 1960s carry an “orphaning”clause, such as “not to be used in children” or “is not recommended for use in infants and young children.”1 In contrast to years past when few studies were conducted in this age group, we are currently witnessing tremendous progress in pediatric drug development. In that context, in the workshop, Drs. Burckart, Stegemann, and Eissing as well as Schlender presented materials related to improving therapeutics to better care for the young. At the other end of the age spectrum, older adult patients are relatively “neglected” with regard to consideration during drug development.2,3 Persons 65 years and above will be the fastest-growing segment of the population in the United States for the next 4 decades primarily because of the migration of the baby boom generation into this age group with a steadily increasing life expectancy. From 2012 to 2050, the projected number of people in the United States aged 65 years and above will almost double to 83.7 million, corresponding to more than 20% of the population.4 This aging trend is consistent with that of developed countries like Japan, Germany, Italy, France, Spain, the United Kingdom, Canada, Ukraine, Poland, and Russia.4 The oldest-old (aged 85 years) segment of the United States is increasing even faster and will triple by 2060.5 Similar aging trends exist in the 3 other most populated countries in the world, namely, China, India, and Indonesia.4 This segment of the older adult population is frailer and is more likely to have significant sensory impairment (hearing and vision), cognitive impairment, and multiple chronic illnesses. Similarly, the incidence of nursing home placement is much higher than among the general older adult population. Although older adults currently account for only 13.1% of the United States population, they consume an estimated 30%–40% of all medications,6 indicating that pharmacotherapy is an important medical intervention for the care of older adult patients. These patients usually have more disease burden and thus receive multiple drug therapies. In that context, in the workshop Drs. Golden, Abernethy, Stegemann, Slattum, and Eissing as well as Schlender presented materials related to improving therapeutics to better care for older adults.

Evaluation of drug-drug interaction (DDI) risk is vital to establish benefit-risk profiles of investigational new drugs during drug development. In vitro experiments are routinely conducted as an important first step to assess metabolism-and transporter-mediated DDI potential of investigational new drugs. Results from these experiments are interpreted, often with the aid of in vitro-in vivo extrapolation methods, to determine whether and how DDI should be evaluated clinically to provide the basis for proper DDI management strategies, including dosing recommendations, alternative therapies, or contraindications under various DDI scenarios and in different patient population. This article provides an overview of currently available in vitro experimental systems and basic in vitroein vivo extrapolation methodologies for metabolism-and transporter-mediated DDIs. Published by Elsevier Inc. on behalf of the American Pharmacists Association.

The impact of cancer therapies on cardiac disease in the general adult cancer survivor population is largely unknown. Our objective was to evaluate which tyrosine kinase-targeting drugs are associated with greater risk for new-onset heart failure (HF). A nested case–control analysis was conducted within a cohort of 27 992 patients of Clalit Health Services, newly treated with a tyrosine kinase-targeting, and/or chemotherapeutic drug, for a malignant disease, between 1 January 2005 and 31 December 2012. Each new case of HF was matched to up to 30 controls from the cohort on calendar year of cohort entry, age, gender, and duration of follow-up. Main outcome measure was odds ratio (OR) with 95% confidence interval (CI) of new-onset HF. There were 936 incident cases of HF during 71 742 person-years of follow-up. Trastuzumab (OR 1.90, 95% CI 1.46–2.49), cetuximab (OR 1.72, 1.10–2.69), panitumumab (OR 3.01, 1.02–8.85), and sunitinib (OR 3.39, 1.78–6.47) were associated with increased HF risk. Comorbidity independently associated with higher risk in a multivariable conditional regression model was diabetes mellitus, hypertension, chronic renal failure, ischaemic heart disease, valvular heart disease, arrhythmia, and smoking. Trastuzumab, cetuximab, panitumumab, and sunitinib are associated with increased risk for new-onset HF.

Recent reviews suggest that chronic kidney disease (CKD) can affect the pharmacokinetics of nonrenally eliminated drugs, but the impact of CKD on individual elimination pathways has not been systematically evaluated. In this study we developed a comprehensive dataset of the effect of CKD on the pharmacokinetics of CYP2D6‐ and CYP3A4/5‐metabolized drugs. Drugs for evaluation were selected based on clinical drug–drug interaction (CYP3A4/5 and CYP2D6) and pharmacogenetic (CYP2D6) studies. Information from dedicated CKD studies was available for 13 and 18 of the CYP2D6 and CYP3A4/5 model drugs, respectively. Analysis of these data suggested that CYP2D6‐mediated clearance is generally decreased in parallel with the severity of CKD. There was no apparent relationship between the severity of CKD and CYP3A4/5‐mediated clearance. The observed elimination‐route dependency in CKD effects between CYP2D6 and CYP3A4/5 may inform the need to conduct clinical CKD studies with nonrenally eliminated drugs for optimal use of drugs in patients with CKD.

The science of quantitative clinical pharmacology continues to advance at a rapid pace such that regulators must constantly evaluate the most appropriate applications of modeling, simulation, and other innovations in the public health context. FDA continues to target improvements in regulatory science, including the development of scientific tools that can bridge the gap between cutting-edge discoveries and real-world diagnostics and therapeutics and to this end has identified innovation through modeling and simulation as a major scientific priority area. Physiological based pharmacokinetic (PBPK) models, which utilize system-and drug-specific information, are being increasingly used during drug discovery and development and informing regulatory review including drug labeling. A multi-step approach may be appropriate when planning to use PBPK to determine the likely effects of drug and/or gene interactions on drug pharmacokinetics and subsequent need for dedicated studies. Published FDA guidance documents related to drug interactions and early phase pharmacogenomic evaluation have included recommendations for the use of PBPK where appropriate. As pharmacology and clinical pharmacology move forward from reductionist approaches toward integrative systems approaches to address problems, efforts are ongoing to leverage the new science that is evolving in systems pharmacology. This is focused on the prediction of adverse drug events using the tools of cheminformatics, bioinformatics, and systems biology. Systems pharmacology will need to be put into the larger translational science context to reach full potential in regulatory decision-making.

Cardiotoxicity is a major concern for the FDA when examining new drug applications (NDAs) for the family of anti-cancer tyrosine kinase inhibitors (TKIs). Despite their therapeutic effect, many TKIs are associated with adverse cardiovascular events (ACEs), including hypertension, decreased ejection fraction, and cardiac failure. Some TKIs are associated with these side effects, while others are not, and little is known about the molecular mechanisms underlying these discrepancies. By integrating different types of molecular data collected by the LINCS consortium, and by others, on the response of human cells to treatment with TKIs, we developed machine learning classifiers that can reliably predict the likelihood that a newly developed TKI will induce cardio-toxicity. We use drug-kinase binding data from an enzyme-liked immunosorbant assay (ELISA), L1000 transcriptional profiling of cancer cell lines, and RNA-seq transcriptional profiling of cardiomyocytes, to identify kinase targets and transcriptional signatures associated with cardiovascular toxicity, thereby unraveling the mechanisms underlying the toxic effects of certain TKIs. The biomarkers we discovered can help determine if newly developed TKIs are likely to induce cardiac complications, suggest mechanisms for such toxicities, to guide further research and assist in regulatory vetting before such adverse events appear in patients.

We lost David Flockhart on Thanksgiving Day, 2015. He was 63 years old, and had battled cancer for more than a year. David Flockhart was a pioneer and innovator in the field of pharmacogenetics. His conceptual contributions to pharmacogenomics date back more than 20 years, when most of us were learning to recognize the word. He is best known for his work on the pharmacogenomics of breast cancer, and the responsiveness of the disease to tamoxifen and aromatase inhibitor treatment. He also made major contributions to understanding the genetic basis for individual variations in pharmacokinetics and clinical response to warfarin, clopidogrel, opiates, antiretroviral drugs, and antidepressants. Despite his conviction of the importance of medical genetics and pharmacogenomics, David retained a critical and questioning outlook, cautioning us about the limitations of the discipline, and the potential downside of widespread or routine genetic profiling. David's scientific work was consistently insightful and of high scientific merit, but his overall objective always was to improve the length and quality of life of ill patients. As such, his impact on both biomedical science and clinical patient care was broad and profound. David was a native of Scotland—the oldest of five children born to D. Ross Flockhart and Pamela Ellison Flockhart. Following his early education in Aberdeen and Edinburgh, he received his undergraduate degree from the University of Bristol in 1973, and his Ph.D. in biochemistry from the Welsh National School of Medicine in 1976. David came to Nashville in 1976, serving first as post-doctoral research associate, then as assistant professor, in the Department of Physiology at Vanderbilt University through 1984. David's soul was set on becoming a physician—he wanted to assure that the science had an impact on humanity. He enrolled at the University of Miami School of Medicine, graduating in 1987. He did his residency training in internal medicine at Georgetown University, finishing as chief medical resident at the Fairfax Hospital in 1991. His voluntary shouldering of the chief residency position—one of medicine's thankless and relentless tasks—says a great deal about David's dedication to patient care and medical education. The same dedication persisted throughout the rest of his career. Following residency, David stayed on for fellowship training in clinical pharmacology at the legendary Georgetown University Division of Clinical Pharmacology, with Ray Woosley as his mentor. David joined the faculty in 1993, and rose to tenured associate professor in 1999. He was named Chair and Chief of the Division in 2000, and was a consistent advocate for and supporter of the discipline of clinical pharmacology. Craig Brater recruited David to Indiana University School of Medicine in 2001, where he became Professor of Medicine, Pharmacology, and Medical Genetics, and Director of Clinical Pharmacology. He was appointed as the Gladstein Chair in Cancer Genomics in 2007, and Director of the Indiana University Institute for Personalized Medicine in 2011. David Flockhart was a national and international leader in cancer pharmacogenomics. He served on or directed numerous scientific symposia, workshops, and policy-making advisory groups. He served as Principal Investigator for the NIH Pharmacogenetics Research Network, and for the Consortium on Breast Cancer Pharmacogenomics. He participated as visiting scientist or guest speaker at the invitation of numerous academic institutions, medical and scientific societies, hospitals, and government agencies. The quality and impact of David's work has been recognized by his peers through awards such as the Rawls-Palmer Progress in Medicine Award from the American Society for Clinical Pharmacology and Therapeutics (2011), and the Nathaniel T. Kwit Distinguished Service Award from the American College of Clinical Pharmacology (2009). With no hesitation, he accepted our invitation to serve on the Editorial Board of Clinical Pharmacology in Drug Development, despite a number of existing editorial board commitments. Above all, David remained devoted to his patients and his students. He was kind and compassionate to the patients under his care, and spent as much time as necessary to assure their best possible treatment. To his students and trainees he was equally devoted, enthusiastic, careful, and thorough. He always believed that scientific advances had to be translated into improved patient care. David loved his family. He loved music, and took great pride in his Scottish beginnings. He retained the quaint Scottish lilt to his speech. David Flockhart was a great scientist, colleague, and friend. We will miss him.

INTRODUCTION:A translational bioinformatics challenge exists in connecting population and individual clinical phenotypes in various formats to biological mechanisms. The Medical Dictionary for Regulatory Activities (MedDRA(®)) is the default dictionary for adverse event (AE) reporting in the US Food and Drug Administration Adverse Event Reporting System (FAERS). The ontology of adverse events (OAE) represents AEs as pathological processes occurring after drug exposures.OBJECTIVES:The aim of this work was to establish a semantic framework to link biological mechanisms to phenotypes of AEs by combining OAE with MedDRA(®) in FAERS data analysis. We investigated the AEs associated with tyrosine kinase inhibitors (TKIs) and monoclonal antibodies (mAbs) targeting tyrosine kinases. The five selected TKIs/mAbs (i.e., dasatinib, imatinib, lapatinib, cetuximab, and trastuzumab) are known to induce impaired ventricular function (non-QT) cardiotoxicity.RESULTS:Statistical analysis of FAERS data identified 1053 distinct MedDRA(®) terms significantly associated with TKIs/mAbs, where 884 did not have corresponding OAE terms. We manually annotated these terms, added them to OAE by the standard OAE development strategy, and mapped them to MedDRA(®). The data integration to provide insights into molecular mechanisms of drug-associated AEs was performed by including linkages in OAE for all related AE terms to MedDRA(®) and the existing ontologies, including the human phenotype ontology (HP), Uber anatomy ontology (UBERON), and gene ontology (GO). Sixteen AEs were shared by all five TKIs/mAbs, and each of 17 cardiotoxicity AEs was associated with at least one TKI/mAb. As an example, we analyzed "cardiac failure" using the relations established in OAE with other ontologies and demonstrated that one of the biological processes associated with cardiac failure maps to the genes associated with heart contraction.CONCLUSION:By expanding the existing OAE ontological design, our TKI use case demonstrated that the combination of OAE and MedDRA(®) provides a semantic framework to link clinical phenotypes of adverse drug events to biological mechanisms.

Drug features that are associated with Stevens-Johnson syndrome (SJS) have not been fully characterized. A molecular target analysis of the drugs associated with SJS in the FDA Adverse Event Reporting System (FAERS) may contribute to mechanistic insights into SJS pathophysiology. The publicly available version of FAERS was analyzed to identify disproportionality among the molecular targets, metabolizing enzymes, and transporters for drugs associated with SJS. The FAERS in-house version was also analyzed for an internal comparison of the drugs most highly associated with SJS. Cyclooxygenases 1 and 2, carbonic anhydrase 2, and sodium channel 2 alpha were identified as disproportionately associated with SJS. Cytochrome P450 (CYPs) 3A4 and 2C9 are disproportionately represented as metabolizing enzymes of the drugs associated with SJS adverse event reports. Multidrug resistance protein 1 (MRP-1), organic anion transporter 1 (OAT1), and PEPT2 were also identified and are highly associated with the transport of these drugs. A detailed review of the molecular targets identifies important roles for these targets in immune response. The association with CYP metabolizing enzymes suggests that reactive metabolites and oxidative stress may have a contributory role. Drug transporters may enhance intracellular tissue concentrations and also have vital physiologic roles that impact keratinocyte proliferation and survival. Data mining FAERS may be used to hypothesize mechanisms for adverse drug events by identifying molecular targets that are highly associated with drug-induced adverse events. The information gained may contribute to systems biology disease models.