Pharmacogenetics of drugs withdrawn from the market

External control arms: COVID-19 reveals the merits of using real world evidence in real-time for clinical and public health investigations

Randomized controlled trials are considered the 'gold standard' to reduce bias by randomizing patients to an experimental intervention, versus placebo or standard of care cohort. There are inherent challenges to enrolling a standard of care or cohorts: costs, site engagement logistics, socioeconomic variability, patient willingness, ethics of placebo interventions, cannibalizing the treatment arm population, and extending study duration. The COVID-19 pandemic has magnified aspects of constraints in trial recruitment and logistics, spurring innovative approaches to reducing trial sizes, accelerating trial accrual while preserving statistical rigor. Using data from medical records and databases allows for construction of external control arms that reduce the costs of an external control arm (ECA) randomized to standard of care. Simultaneously examining covariates of the clinical outcomes in ECAs that are being measured in the interventional arm can be particularly useful in phase 2 trials to better understand social and genetic determinants of clinical outcomes that might inform pivotal trial design. The FDA and EMA have promulgated a number of publicly available guidance documents and qualification reports that inform the use of this regulatory science tool to streamline clinical development, of phase 4 surveillance, and policy aspects of clinical outcomes research. Availability and quality of real-world data (RWD) are a prevalent impediment to the use of ECAs given such data is not collected with the rigor and deliberateness that characterizes prospective interventional control arm data. Conversely, in the case of contemporary control arms, a clinical trial outcome can be compared to a contemporary standard of care in cases where the standard of care is evolving at a fast pace, such as the use of checkpoint inhibitors in cancer care. Innovative statistical methods are an essential aspect of an ECA strategy and regulatory paths for these innovative approaches have been navigated, qualified, and in some cases published.

In Silico Analyses of a Promising Drug Candidate for the Treatment of Amyotrophic Lateral Sclerosis Targeting Superoxide Dismutase I Protein

Amyotrophic lateral sclerosis (ALS) is the most prevalent motor neuron disorder in adults, which is associated with a highly disabling condition. To date, ALS remains incurable, and the only drugs approved by the FDA for its treatment confer a limited survival benefit. Recently, SOD1 binding ligand 1 (SBL-1) was shown to inhibit in vitro the oxidation of a critical residue for SOD1 aggregation, which is a central event in ALS-related neurodegeneration. In this work, we investigated the interactions between SOD1 wild-type and its most frequent variants, i.e., A4V (NP_000445.1:p.Ala5Val) and D90A (NP_000445.1:p.Asp91Val), with SBL-1 using molecular dynamics (MD) simulations. The pharmacokinetics and toxicological profile of SBL-1 were also characterized in silico. The MD results suggest that the complex SOD1-SBL-1 remains relatively stable and interacts within a close distance during the simulations. This analysis also suggests that the mechanism of action proposed by SBL-1 and its binding affinity to SOD1 may be preserved upon mutations A4V and D90A. The pharmacokinetics and toxicological assessments suggest that SBL-1 has drug-likeness characteristics with low toxicity. Our findings, therefore, suggested that SBL-1 may be a promising strategy to treat ALS based on an unprecedented mechanism, including for patients with these frequent mutations.

Facing small and biased data dilemma in drug discovery with enhanced federated learning approaches

Artificial intelligence (AI) models usually require large amounts of high-quality training data, which is in striking contrast to the situation of small and biased data faced by current drug discovery pipelines. The concept of federated learning has been proposed to utilize distributed data from different sources without leaking sensitive information of the data. This emerging decentralized machine learning paradigm is expected to dramatically improve the success rate of AI-powered drug discovery. Here, we simulated the federated learning process with different property and activity datasets from different sources, among which overlapping molecules with high or low biases exist in the recorded values. Beyond the benefit of gaining more data, we also demonstrated that federated training has a regularization effect superior to centralized training on the pooled datasets with high biases. Moreover, different network architectures for clients and aggregation algorithms for coordinators have been compared on the performance of federated learning, where personalized federated learning shows promising results. Our work demonstrates the applicability of federated learning in predicting drug-related properties and highlights its promising role in addressing the small and biased data dilemma in drug discovery.

Modeling the Bioactivation and Subsequent Reactivity of Drugs

Electrophilically reactive drug metabolites are implicated in many adverse drug reactions. In this mechanism-termed bioactivation-metabolic enzymes convert drugs into reactive metabolites that often conjugate to nucleophilic sites within biological macromolecules like proteins. Toxic metabolite-product adducts induce severe immune responses that can cause sometimes fatal disorders, most commonly in the form of liver injury, blood dyscrasia, or the dermatologic conditions toxic epidermal necrolysis and Stevens-Johnson syndrome. This study models four of the most common metabolic transformations that result in bioactivation: quinone formation, epoxidation, thiophene sulfur-oxidation, and nitroaromatic reduction, by synthesizing models of metabolism and reactivity. First, the metabolism models predict the formation probabilities of all possible metabolites among the pathways studied. Second, the exact structures of these metabolites are enumerated. Third, using these structures, the reactivity model predicts the reactivity of each metabolite. Finally, a feedfoward neural network converts the metabolism and reactivity predictions to a bioactivation prediction for each possible metabolite. These bioactivation predictions represent the joint probability that a metabolite forms and that this metabolite subsequently conjugates to protein or glutathione. Among molecules bioactivated by these pathways, we predicted the correct pathway with an AUC accuracy of 89.98%. Furthermore, the model predicts whether molecules will be bioactivated, distinguishing bioactivated and nonbioactivated molecules with 81.06% AUC. We applied this algorithm to withdrawn drugs. The known bioactivation pathways of alclofenac and benzbromarone were identified by the algorithm, and high probability bioactivation pathways not yet confirmed were identified for safrazine, zimelidine, and astemizole. This bioactivation model-the first of its kind that jointly considers both metabolism and reactivity-enables drug candidates to be quickly evaluated for a toxicity risk that often evades detection during preclinical trials. The XenoSite bioactivation model is available at http://swami.wustl.edu/xenosite/p/bioactivation.

A possible increase in liver enzymes due to amlodipine: A case report

Amlodipine is a commonly prescribed antihypertensive drug, well tolerated and has rarely been attributed as a cause for elevated liver enzymes. Here, we present a 47-year-old male patient known to be hypertensive and admitted to our rehabilitation facility after an acute stroke. During his stay, amlodipine was started in addition to other antihypertensive medications to control his blood pressure. His liver transaminases after 4 days (notably alanine aminotransferase) were found to be markedly elevated. After reviewing the medications and investigating probable causes, amlodipine was suspended. After 5 days of suspending amlodipine, the transaminases started to trend downward. The Naranjo Adverse Drug Reaction Probability Scale and the Roussel Uclaf Causality Assessment Method were performed to assess causality in this suspected idiosyncratic drug-induced liver injury case. Both the scores denoted a probable amlodipine-induced liver injury. Previous case reports related to amlodipine-induced liver injury are mentioned and presented in the table below. In conclusion, amlodipine, though not well known to be hepatotoxic, can induce liver enzyme elevations in an idiosyncratic manner.

Capsule Networks Showed Excellent Performance in the Classification of hERG Blockers/Nonblockers

Capsule networks (CapsNets), a new class of deep neural network architectures proposed recently by Hinton et al., have shown a great performance in many fields, particularly in image recognition and natural language processing. However, CapsNets have not yet been applied to drug discovery-related studies. As the first attempt, we in this investigation adopted CapsNets to develop classification models of hERG blockers/nonblockers; drugs with hERG blockade activity are thought to have a potential risk of cardiotoxicity. Two capsule network architectures were established: convolution-capsule network (Conv-CapsNet) and restricted Boltzmann machine-capsule networks (RBM-CapsNet), in which convolution and a restricted Boltzmann machine (RBM) were used as feature extractors, respectively. Two prediction models of hERG blockers/nonblockers were then developed by Conv-CapsNet and RBM-CapsNet with the Doddareddy's training set composed of 2,389 compounds. The established models showed excellent performance in an independent test set comprising 255 compounds, with prediction accuracies of 91.8 and 92.2% for Conv-CapsNet and RBM-CapsNet models, respectively. Various comparisons were also made between our models and those developed by other machine learning methods including deep belief network (DBN), convolutional neural network (CNN), multilayer perceptron (MLP), support vector machine (SVM), k-nearest neighbors (kNN), logistic regression (LR), and LightGBM, and with different training sets. All the results showed that the models by Conv-CapsNet and RBM-CapsNet are among the best classification models. Overall, the excellent performance of capsule networks achieved in this investigation highlights their potential in drug discovery-related studies.

Comparison of Zebrafish Larvae and hiPSC Cardiomyocytes for Predicting Drug-Induced Cardiotoxicity in Humans

Cardiovascular drug toxicity is responsible for 17% of drug withdrawals in clinical phases, half of post-marketed drug withdrawals and remains an important adverse effect of several marketed drugs. Early assessment of drug-induced cardiovascular toxicity is mandatory and typically done in cellular systems and mammals. Current in vitro screening methods allow high-throughput but are biologically reductionist. The use of mammal models, which allow a better translatability for predicting clinical outputs, is low-throughput, highly expensive, and ethically controversial. Given the analogies between the human and the zebrafish cardiovascular systems, we propose the use of zebrafish larvae during early drug discovery phases as a balanced model between biological translatability and screening throughput for addressing potential liabilities. To this end, we have developed a high-throughput screening platform that enables fully automatized in vivo image acquisition and analysis to extract a plethora of relevant cardiovascular parameters: heart rate, arrhythmia, AV blockage, ejection fraction, and blood flow, among others. We have used this platform to address the predictive power of zebrafish larvae for detecting potential cardiovascular liabilities in humans. We tested a chemical library of 92 compounds with known clinical cardiotoxicity profiles. The cross-comparison with clinical data and data acquired from human induced pluripotent stem cell cardiomyocytes calcium imaging showed that zebrafish larvae allow a more reliable prediction of cardiotoxicity than cellular systems. Interestingly, our analysis with zebrafish yields similar predictive performance as previous validation meta-studies performed with dogs, the standard regulatory preclinical model for predicting cardiotoxic liabilities prior to clinical phases.

Discovery of Orexant and Anorexant Agents with Indazole Scaffold Endowed with Peripheral Antiedema Activity

The endocannabinoid system represents an integrated neuronal network involved in the control of several organisms' functions, such as feeding behavior. A series of hybrids of 5-(4-chlorophenyl)-1-(2,4-dichloro-phenyl)-4-methyl-N-(piperidin-1-yl)-1H-pyrazole-3-carboxamide (mimonabant), a well-known inverse agonist of the type-1 cannabinoid receptor (CB1), once used as an antiobesity drug, and the N-(2S)-substitutes of 1-[(4-fluorophenyl)methyl]indazole-3-carboxamide with 1-amino-3-methyl-1-oxobutane (AB-Fubinaca), 1-amino-3,3-dimethyl-1-oxobutane (ADB-Fubinaca), and 3-methylbutanoate (AMB-Fubinaca), endowed with potent agonistic activity towards cannabinoid receptors CB1 and CB2 were in solution as C-terminal amides, acids, methyl esters and N-methyl amides. These compounds have been studied by binding assays to cannabinoid receptors and by functional receptor assays, using rat brain membranes in vitro. The most active among them as an agonist, (S)-1-(2,4-dichlorobenzyl)-N-(3,3-dimethyl-1-(methylamino)-1-oxobutan-2-yl)-1H-indazole-3-carboxamide (LONI11), and an antagonist, (S)-2-(1-(2,4-dichlorobenzyl)-1H-indazole-3-carboxamido)-3-methylbutanoic acid (LONI4), were tested in vivo in mic, to evaluate their ability to stimulate or suppress feeding behavior after intraperitoneal (i.p.) administration. For a LONI11 formalin test and a tail flick test after an administration by the subcutaneous (s.c.) and intracerebroventricular (i.c.v.) routes, respectively, were also carried out in vivo in mice to investigate the antinociceptive property at the central and peripheral levesl. We observed a significant orexant effect for LONI11 and an intense anorexant effect for (S)-methyl 2-(1-(2,4-dichlorobenzyl)-1H-indazole-3-carboxamido)-3,3-dimethylbutanoate (LONI2) and LONI4. In zymosan-induced edema and hyperalgesia, LONI11 reduced the percent of paw volume increase and paw latency after s.c. administration, also suggesting a possible peripheral anti-inflammatory activity.

Medicine in the Fourth Dimension

The importance of circadian biology has rarely been considered in pre-clinical studies, and even more when translating to the bedside. Circadian biology is becoming a critical factor for improving drug efficacy and diminishing drug toxicity. Indeed, there is emerging evidence showing that some drugs are more effective at nighttime than daytime, whereas for others it is the opposite. This suggests that the biology of the target cell will determine how an organ will respond to a drug at a specific time of the day, thus modulating pharmacodynamics. Thus, it is now time that circadian factors become an integral part of translational research.

African Pharmacogenomics Consortium: Consolidating pharmacogenomics knowledge, capacity development and translation in Africa: Consolidating pharmacogenomics knowledge, capacity development and translation in Africa

The African Pharmacogenomics Consortium (APC) was formally launched on the 6th September 2018. This white paper outlines its vision, and objectives towards addressing challenges of conducting and applying pharmacogenomics in Africa and identifies opportunities for advancement of individualized drugs use on the continent.  Africa, especially south of the Sahara, is beset with a huge burden of infectious diseases with much co-morbidity whose multiplicity and intersection are major challenges in achieving the sustainable development goals (SDG), SDG3, on health and wellness. The profile of drugs commonly used in African populations lead to a different spectrum of adverse drug reactions (ADRs) when compared to other parts of the world. Coupled with the genetic diversity among Africans, the APC is established to promote pharmacogenomics research and its clinical implementation for safe and effective use of medicine in the continent.  Variation in the way patients respond to treatment is mainly due to differences in activity of enzymes and transporters involved in pathways associated with each drug’s disposition.  Knowledge of pharmacogenomics, therefore, helps in identifying genetic variants in these proteins and their functional effects. Africa needs to consolidate its pharmacogenomics expertise and technological platforms to bring pharmacogenomics to use.

Road Map for Development of Stem Cell-Based Alternative Test Methods

Much progress has been made in establishing strategies for differentiation of induced human pluripotent stem cells (hiPSCs). However, differentiated hiPSCs are not yet routinely used for prediction of toxicity. Here, limiting factors are summarised and possibilities for improvement are discussed, with a focus on hepatocytes, cardiomyocytes, tubular epithelial cells, and developmental toxicity. Moreover, we make recommendations for further fine-tuning of differentiation protocols for hiPSCs to hepatocyte-like cells by comparing individual steps of currently available protocols to the mechanisms occurring during embryonic development. A road map is proposed to facilitate test system development, including a description of the most useful performance metrics.

Organoids as a new model for improving regenerative medicine and cancer personalized therapy in renal diseases

The pressure towards innovation and creation of new model systems in regenerative medicine and cancer research has fostered the development of novel potential therapeutic applications. Kidney injuries provoke a high request of organ transplants making it the most demanding system in the field of regenerative medicine. Furthermore, renal cancer frequently threaten patients’ life and aggressive forms still remain difficult to treat. Ethical issues related to the use of embryonic stem cells, has fueled research on adult, patient-specific pluripotent stem cells as a model for discovery and therapeutic development, but to date, normal and cancerous renal experimental models are lacking. Several research groups are focusing on the development of organoid cultures. Since organoids mimic the original tissue architecture in vitro, they represent an excellent model for tissue engineering studies and cancer therapy testing. We established normal and tumor renal cell carcinoma organoids previously maintained in a heterogeneous multi-clone stem cell-like enriching medium. Starting from adult normal kidney specimens, we were able to isolate and propagate organoid 3D-structures composed of both differentiated and undifferentiated cells while expressing nephron specific markers. Furthermore, we were capable to establish organoids derived from cancer tissues although with a success rate inferior to that of their normal counterpart. Cancer cultures displayed epithelial and mesenchymal phenotype while retaining tumor specific markers. Of note, tumor organoids recapitulated neoplastic masses when orthotopically injected into immunocompromised mice. Our data suggest an innovative approach of long-term establishment of normal- and cancer-derived renal organoids obtained from cultures of fleshly dissociated adult tissues. Our results pave the way to organ replacement pioneering strategies as well as to new models for studying drug-induced nephrotoxicity and renal diseases. Along similar lines, deriving organoids from renal cancer patients opens unprecedented opportunities for generation of preclinical models aimed at improving therapeutic treatments.

Genetic polymorphisms in very important pharmacogenomic variants in the Zhuang ethnic group of Southwestern China: A cohort study in the Zhuang population

Pharmacogenomics, the study of the role of genetics in drug response, has recently become a focal point of research. Previous studies showed that genes associated with drug detoxification vary among different populations. However, pharmacogenomic information of the Zhuang ethnic group is scarce. The aim of the present study was to screen members of the Zhuang ethnicity in southwestern China for genotype frequencies of very important pharmacogenomic (VIP) variants and to determine the differences between the Zhuang ethnicity and other human populations.We genotyped 80 variants of VIP genes in 100 unrelated healthy Zhuang adults from the Yunnan province of China. Next, we analyzed the genotyping data with Structure and F-statistics (Fst).We compared our data with those of other populations using the HapMap data set, and observed that the frequency distribution of Zhuang population in Yunnan closely resembles that of JPT. Furthermore, population structure and Fst analysis showed that the Zhuang population is closely related to the Shaanxi Han population with respect to genetic background.Our study supplements existing information on Zhuang population pharmacogenomics and provides an extensive overview for developing personalized medicine.

Epidemiology and Genetic Risk Factors of Drug Hepatotoxicity

Idiosyncratic drug-induced liver injury (DILI) from prescription medications and herbal and dietary supplements has an annual incidence rate of approximately 20 cases per 100,000 per year. However, the risk of DILI varies greatly according to the drug. In the United States and Europe, antimicrobials are the commonest implicated agents, with amoxicillin/clavulanate the most common, whereas in Asian countries, herbal and dietary supplements predominate. Genetic analysis of DILI is currently limited, but multiple polymorphisms of human leukocyte antigen genes and genes involved in drug metabolism and transport have been identified as risk factors for DILI.

Optimising case detection within UK electronic health records: use of multiple linked databases for detecting liver injury

OBJECTIVES

We aimed to create a 'multidatabase' algorithm for identification of cholestatic liver injury using multiple linked UK databases, before (1) assessing the improvement in case ascertainment compared to using a single database and (2) developing a new single-database case-definition algorithm, validated against the multidatabase algorithm.

DESIGN

Method development for case ascertainment.

SETTING

Three UK population-based electronic health record databases: the UK Clinical Practice Research Datalink (CPRD), the UK Hospital Episodes Statistics (HES) database and the UK Office of National Statistics (ONS) mortality database.

PARTICIPANTS

16 040 people over the age of 18 years with linked CPRD-HES records indicating potential cholestatic liver injury between 1 January 2000 and 1 January 2013.

PRIMARY OUTCOME MEASURES

(1) The number of cases of cholestatic liver injury detected by the multidatabase algorithm. (2) The relative contribution of each data source to multidatabase case status. (3) The ability of the new single-database algorithm to discriminate multidatabase algorithm case status.

RESULTS

Within the multidatabase case identification algorithm, 4033 of 16 040 potential cases (25%) were identified as definite cases based on CPRD data. HES data allowed possible cases to be discriminated from unlikely cases (947 of 16 040, 6%), but only facilitated identification of 1 definite case. ONS data did not contribute to case definition. The new single-database (CPRD-only) algorithm had a very good ability to discriminate multidatabase case status (area under the receiver operator characteristic curve 0.95).

CONCLUSIONS

CPRD-HES-ONS linkage confers minimal improvement in cholestatic liver injury case ascertainment compared to using CPRD data alone, and a multidatabase algorithm provides little additional information for validation of a CPRD-only algorithm. The availability of laboratory test results within CPRD but not HES means that algorithms based on CPRD-HES-linked data may not always be merited for studies of liver injury, or for other outcomes relying primarily on laboratory test results.

Dosing-Time Makes the Poison: Circadian Regulation and Pharmacotherapy

Daily rhythms in physiology significantly modulate drug pharmacokinetics and pharmacodynamics according to the time-of-day, a finding that has led to the concept of chronopharmacology. The importance of biological clocks for xenobiotic metabolism has gained increased attention with the discovery of the molecular circadian clockwork. Mechanistic understanding of the cell-autonomous molecular circadian oscillator and the circadian timing system as a whole has opened new conceptual and methodological lines of investigation to understand first, the clock's impact on a specific drug's daily variations or the effects/side effects of environmental substances, and second, how clock-controlled pathways are coordinated within a given tissue or organism. Today, there is an increased understanding of the circadian modulation of drug effects. Moreover, several molecular strategies are being developed to treat disease-dependent and drug-induced clock disruptions in humans.

Genetics and pharmacogenetics of aminergic transmitter pathways in functional gastrointestinal disorders

Functional gastrointestinal disorders (FGIDs) are highly prevalent syndromes, without evident underlying organic causes. Their pathogenesis is multifactorial in nature, with a combination of environmental and genetic factors contributing to their clinical manifestations, for which most of current treatments are not satisfactory. It is acknowledged that amine mediators (noradrenaline, dopamine and serotonin) play pivotal regulatory actions on gut functions and visceral sensation. In addition, drugs of therapeutic interest for FGIDs act on these transmitter pathways. The present article reviews current knowledge on the impact of genetics and pharmacogenetics of aminergic pathways on FGID pathophysiology, clinical presentations, symptom severity and medical management, in an attempt of highlighting the most relevant evidence and point out issues that should be addressed in future investigations.

WITHDRAWN--a resource for withdrawn and discontinued drugs

Post-marketing drug withdrawals can be associated with various events, ranging from safety issues such as reported deaths or severe side-effects, to a multitude of non-safety problems including lack of efficacy, manufacturing, regulatory or business issues. During the last century, the majority of drugs voluntarily withdrawn from the market or prohibited by regulatory agencies was reported to be related to adverse drug reactions. Understanding the underlying mechanisms of toxicity is of utmost importance for current and future drug discovery. Here, we present WITHDRAWN, a resource for withdrawn and discontinued drugs publicly accessible at http://cheminfo.charite.de/withdrawn. Today, the database comprises 578 withdrawn or discontinued drugs, their structures, important physico-chemical properties, protein targets and relevant signaling pathways. A special focus of the database lies on the drugs withdrawn due to adverse reactions and toxic effects. For approximately one half of the drugs in the database, safety issues were identified as the main reason for withdrawal. Withdrawal reasons were extracted from the literature and manually classified into toxicity types representing adverse effects on different organs. A special feature of the database is the presence of multiple search options which will allow systematic analyses of withdrawn drugs and their mechanisms of toxicity.

An analysis of the use of animal models in predicting human toxicology and drug safety

Animal use continues to be central to preclinical drug development, in spite of a lack of its demonstrable validity. The current nadir of new drug approvals and the drying-up of pipelines may be a direct consequence of this. To estimate the evidential weight given by animal data to the probability that a new drug may be toxic to humans, we have calculated Likelihood Ratios (LRs) for an extensive data set of 2,366 drugs, for which both animal and human data are available, including tissue-level effects and MedDRA Level 1-4 biomedical observations. This was done for three preclinical species (rat, mouse and rabbit), to augment our previously-published analysis of canine data. In common with our dog analysis, the resulting LRs show: a) that the absence of toxicity in the animal provides little or virtually no evidential weight that adverse drug reactions (ADRs) will also be absent in humans; and b) that, while the presence of toxicity in these species can add considerable evidential weight for human risk, the LRs are extremely inconsistent, varying by over two orders of magnitude for different classes of compounds and their effects. Therefore, our results for these additional preclinical species have important implications for their use in predicting human toxicity, and suggest that alternative methods are urgently required.

Pharmacogenovigilance: a pharmacogenomics pharmacovigilance program

In this report, we review the importance of pharmacovigilance in detecting postmarketing adverse drug events and the potential for developing pharmacogenovigilance programs by integrating pharmacogenomics with pharmacovigilance. We propose to start developing such a program in primary healthcare systems that use basic features of electronic medical records and have access to large numbers of patients commonly prescribed drugs. Such programs, if carefully designed, may grow over time and hopefully enhance the collection and interpretation of useful data for the clinical applications of pharmacogenomics testing.

Time-series pattern of gene expression profile in gentamycin-induced nephrotoxicity

There have been many studies investigating the genomic biomarker and/or molecular mechanism of nephrotoxicity using microarray. However, most of these researches were carried out by studying gene expression changes at one specific time point. As gene expression varies with time and disease stage, the current study investigated the time-series pattern of gene expression in a rat model using a typical nephrotoxic compound. Rats were administrated with 80 mg/kg gentamycin or saline by intramuscular injection for 14 consecutive days followed by a 28-d recovery. Rats were scarified on D2, D4, D8, D15 and Recovery Day (R29), when kidneys were obtained for whole-genome microarray analysis and histological examination. Urine was collected at each necropsy for kidney injury molecular-1 (KIM-1) analysis. The KIM-1 detection and histological examination confirmed the nephrotoxicity. After differentially expression genes (DEGs) identification, there were 4360 and 4323 regulated genes for females and males, respectively. However, few overlapping expression genes co-regluated at each time point were found. By principle component analysis (PCA) and hierarchical cluster, the gene expression patterns were observed to be apparently associated with the disease stage. GO Annotation showed (1) immune response and related process, response to wounding, cell locomotion on D2; (2) cell death and apoptosis was also noted on D4; (3) processes of organic acid or carboxylic acid, apoptosis or cell death on D8 and D15; (4) processes of cell cycle, mitosis, division cell cycle on R29. In conclusion, the authors mapped the time-series gene expression patterns at the initiation, development and recovery stage of gentamycin-induced nephrotoxicity.

Development of predictive genetic tests for improving the safety of new medicines: the utilization of routinely collected electronic health records

Serious adverse drug reactions are an important cause of hospitalization and can result in the withdrawal of licensed drugs. Genetic variation has been shown to influence adverse drug reaction susceptibility, and predictive genetic tests have been developed for a limited number of adverse drug reactions. The identification of patients with adverse drug reactions, obtaining samples for genetic analysis and rigorous evaluation of clinical test effectiveness represent significant challenges to predictive genetic test development. Using the example of serious drug-induced liver injury, we illustrate how a database of routinely collected electronic health records (EHRs) could be used to overcome these barriers by facilitating rapid recruitment to genome-wide association studies and supporting efficient randomized controlled trials of predictive genetic test effectiveness.

Consistency of drug-drug and gene-drug interaction information in US FDA-approved drug labels

AIM

To characterize concordance between clinically relevant drug-drug interactions (DDIs) related to CYP2C19, CYP2D6 and CYP2C9 and their analogous gene-drug interactions (GDIs) in US FDA-approved drug labeling.

METHODS

We selected prototypical CYP2C19, CYP2D6 and CYP2C9 inhibitors and abstracted all respective interacting drugs via a tertiary resource used in the clinical setting. We then selected only CYP2C19, CYP2D6 and CYP2C9 metabolism-related DDIs requiring enhanced clinical monitoring, dose adjustment or use of alternative drugs. Labeling and management strategies on DDIs and GDIs were compared.

RESULTS

Among the drug labels with DDI information, 73% of them describe the analogous GDI. Of the 65 drug labels, 43 and 17% had specific management recommendations for DDIs and GDIs, respectively. In general, GDI management recommendations were concordant with DDI management recommendations in terms of specific dose adjustments or use of alternative drugs.

CONCLUSION

The FDA has recognized genetic differences in drug metabolism where clinically relevant DDIs trigger dose adjustment or use of alternative drugs.

Current pharmacogenomic studies on hERG potassium channels

Genetic polymorphisms in human ether-a-go-go-related gene (hERG) potassium channels are associated with many complex diseases and sensitivity to channel-related drugs. Genotypes may underlie different sensitivities to the same drug, and different drugs selectively repair the functional deficits caused by individual mutations. In fact, not all drugs that block hERG function have adverse effects as previously thought. This suggests that the severe adverse reactions observed clinically may only occur in subjects with a particular genotype, but to others may be safe. Similarly, a drug that is ineffective in one population may be both safe and effective in another. Therefore, detecting polymorphisms in KCNH2 encoding hERG1 is of great significance in guiding the prevention and treatment of related diseases, re-evaluating drug safety, and individualizing treatment. This article reviews current pharmacogenomic studies on hERG potassium channels to provide a reference for developing individualized treatments and evaluating their safety.

Pharmacogenomics in clinical practice and drug development

Genome-wide association studies (GWAS) of responses to drugs, including clopidogrel, pegylated-interferon and carbamazepine, have led to the identification of specific patient subgroups that benefit from therapy. However, the identification and replication of common sequence variants that are associated with either efficacy or safety for most prescription medications at odds ratios (ORs) >3.0 (equivalent to >300% increased efficacy or safety) has yet to be translated to clinical practice. Although some of the studies have been completed, the results have not been incorporated into therapy, and a large number of commonly used medications have not been subject to proper pharmacogenomic analysis. Adoption of GWAS, exome or whole genome sequencing by drug development and treatment programs is the most striking near-term opportunity for improving the drug candidate pipeline and boosting the efficacy of medications already in use.