Prescription drugs and mitochondrial metabolism.

Mitochondria are central to the physiology and survival of nearly all eukaryotic cells and house diverse metabolic processes including oxidative phosphorylation, reactive oxygen species buffering, metabolite synthesis/exchange, and Ca2+ sequestration. Mitochondria are phenotypically heterogeneous and this variation is essential to the complexity of physiological function among cells, tissues, and organ systems. As a consequence of mitochondrial integration with so many physiological processes, small molecules that modulate mitochondrial metabolism induce complex systemic effects. In the case of many commonly prescribed drugs, these interactions may contribute to drug therapeutic mechanisms, induce adverse drug reactions, or both. The purpose of this article is to review historical and recent advances in the understanding of the effects of prescription drugs on mitochondrial metabolism. Specific 'modes' of xenobiotic-mitochondria interactions are discussed to provide a set of qualitative models that aid in conceptualizing how the mitochondrial energy transduction system may be affected. Findings of recent in vitro high-throughput screening studies are reviewed, and a few candidate drug classes are chosen for additional brief discussion (i.e. antihyperglycemics, antidepressants, antibiotics, and antihyperlipidemics). Finally, recent improvements in pharmacokinetics models that aid in quantifying systemic effects of drug-mitochondria interactions are briefly considered.

Microphysiological systems in absorption, distribution, metabolism, and elimination sciences.

The use of microphysiological systems (MPS) to support absorption, distribution, metabolism, and elimination (ADME) sciences has grown substantially in the last decade, in part driven by regulatory demands to move away from traditional animal-based safety assessment studies and industry desires to develop methodologies to efficiently screen and characterize drugs in the development pipeline. The past decade of MPS development has yielded great user-driven technological advances with the collective fine-tuning of cell culture techniques, fluid delivery systems, materials engineering, and performance enhancing modifications. The rapid advances in MPS technology have now made it feasible to evaluate critical ADME parameters within a stand-alone organ system or through interconnected organ systems. This review surveys current MPS developed for liver, kidney, and intestinal systems as stand-alone or interconnected organ systems, and evaluates each system for specific performance criteria recommended by regulatory authorities and MPS leaders that would render each system suitable for evaluating drug ADME. Whereas some systems are more suitable for ADME type research than others, not all system designs were intended to meet the recently published desired performance criteria and are reported as a summary of initial proof-of-concept studies.

Massively parallel characterization of CYP2C9 variant enzyme activity and abundance.

CYP2C9 encodes a cytochrome P450 enzyme responsible for metabolizing up to 15% of small molecule drugs, and CYP2C9 variants can alter the safety and efficacy of these therapeutics. In particular, the anti-coagulant warfarin is prescribed to over 15 million people annually and polymorphisms in CYP2C9 can affect individual drug response and lead to an increased risk of hemorrhage. We developed click-seq, a pooled yeast-based activity assay, to test thousands of variants. Using click-seq, we measured the activity of 6,142 missense variants in yeast. We also measured the steady-state cellular abundance of 6,370 missense variants in a human cell line by using variant abundance by massively parallel sequencing (VAMP-seq). These data revealed that almost two-thirds of CYP2C9 variants showed decreased activity and that protein abundance accounted for half of the variation in CYP2C9 function. We also measured activity scores for 319 previously unannotated human variants, many of which may have clinical relevance.

INTEDE: interactome of drug-metabolizing enzymes.

Drug-metabolizing enzymes (DMEs) are critical determinant of drug safety and efficacy, and the interactome of DMEs has attracted extensive attention. There are 3 major interaction types in an interactome: microbiome-DME interaction (MICBIO), xenobiotics-DME interaction (XEOTIC) and host protein-DME interaction (HOSPPI). The interaction data of each type are essential for drug metabolism, and the collective consideration of multiple types has implication for the future practice of precision medicine. However, no database was designed to systematically provide the data of all types of DME interactions. Here, a database of the Interactome of Drug-Metabolizing Enzymes (INTEDE) was therefore constructed to offer these interaction data. First, 1047 unique DMEs (448 host and 599 microbial) were confirmed, for the first time, using their metabolizing drugs. Second, for these newly confirmed DMEs, all types of their interactions (3359 MICBIOs between 225 microbial species and 185 DMEs; 47 778 XEOTICs between 4150 xenobiotics and 501 DMEs; 7849 HOSPPIs between 565 human proteins and 566 DMEs) were comprehensively collected and then provided, which enabled the crosstalk analysis among multiple types. Because of the huge amount of accumulated data, the INTEDE made it possible to generalize key features for revealing disease etiology and optimizing clinical treatment. INTEDE is freely accessible at: https://idrblab.org/intede/.

Drugs Commonly Applied to Kidney Patients May Compromise Renal Tubular Uremic Toxins Excretion.

In chronic kidney disease (CKD), the secretion of uremic toxins is compromised leading to their accumulation in blood, which contributes to uremic complications, in particular cardiovascular disease. Organic anion transporters (OATs) are involved in the tubular secretion of protein-bound uremic toxins (PBUTs). However, OATs also handle a wide range of drugs, including those used for treatment of cardiovascular complications and their interaction with PBUTs is unknown. The aim of this study was to investigate the interaction between commonly prescribed drugs in CKD and endogenous PBUTs with respect to OAT1-mediated uptake. We exposed a unique conditionally immortalized proximal tubule cell line (ciPTEC) equipped with OAT1 to a panel of selected drugs, including angiotensin-converting enzyme inhibitors (ACEIs: captopril, enalaprilate, lisinopril), angiotensin receptor blockers (ARBs: losartan and valsartan), furosemide and statins (pravastatin and simvastatin), and evaluated the drug-interactions using an OAT1-mediated fluorescein assay. We show that selected ARBs and furosemide significantly reduced fluorescein uptake, with the highest potency for ARBs. This was exaggerated in presence of some PBUTs. Selected ACEIs and statins had either no or a slight effect at supratherapeutic concentrations on OAT1-mediated fluorescein uptake. In conclusion, we demonstrate that PBUTs may compete with co-administrated drugs commonly used in CKD management for renal OAT1 mediated secretion, thus potentially compromising the residual renal function.

Incorporating chemical sub-structures and protein evolutionary information for inferring drug-target interactions.

Accumulating evidence has shown that drug-target interactions (DTIs) play a crucial role in the process of genomic drug discovery. Although biological experimental technology has made great progress, the identification of DTIs is still very time-consuming and expensive nowadays. Hence it is urgent to develop in silico model as a supplement to the biological experiments to predict the potential DTIs. In this work, a new model is designed to predict DTIs by incorporating chemical sub-structures and protein evolutionary information. Specifically, we first use Position-Specific Scoring Matrix (PSSM) to convert the protein sequence into the numerical descriptor containing biological evolutionary information, then use Discrete Cosine Transform (DCT) algorithm to extract the hidden features and integrate them with the chemical sub-structures descriptor, and finally utilize Rotation Forest (RF) classifier to accurately predict whether there is interaction between the drug and the target protein. In the 5-fold cross-validation (CV) experiment, the average accuracy of the proposed model on the benchmark datasets of Enzymes, Ion Channels, GPCRs and Nuclear Receptors reached 0.9140, 0.8919, 0.8724 and 0.8111, respectively. In order to fully evaluate the performance of the proposed model, we compare it with different feature extraction model, classifier model, and other state-of-the-art models. Furthermore, we also implemented case studies. As a result, 8 of the top 10 drug-target pairs with the highest prediction score were confirmed by related databases. These excellent results indicate that the proposed model has outstanding ability in predicting DTIs and can provide reliable candidates for biological experiments.

Drug-nutrient interactions: discovering prescription drug inhibitors of the thiamine transporter ThTR-2 (SLC19A3).

BACKGROUND: Transporter-mediated drug-nutrient interactions have the potential to cause serious adverse events. However, unlike drug-drug interactions, these drug-nutrient interactions receive little attention during drug development. The clinical importance of drug-nutrient interactions was highlighted when a phase III clinical trial was terminated due to severe adverse events resulting from potent inhibition of thiamine transporter 2 (ThTR-2; SLC19A3). OBJECTIVE: In this study, we tested the hypothesis that therapeutic drugs inhibit the intestinal thiamine transporter ThTR-2, which may lead to thiamine deficiency. METHODS: For this exploration, we took a multifaceted approach, starting with a high-throughput in vitro primary screen to identify inhibitors, building in silico models to characterize inhibitors, and leveraging real-world data from electronic health records to begin to understand the clinical relevance of these inhibitors. RESULTS: Our high-throughput screen of 1360 compounds, including many clinically used drugs, identified 146 potential inhibitors at 200 µM. Inhibition kinetics were determined for 28 drugs with half-maximal inhibitory concentration (IC50) values ranging from 1.03 µM to >1 mM. Several oral drugs, including metformin, were predicted to have intestinal concentrations that may result in ThTR-2-mediated drug-nutrient interactions. Complementary analysis using electronic health records suggested that thiamine laboratory values are reduced in individuals receiving prescription drugs found to significantly inhibit ThTR-2, particularly in vulnerable populations (e.g., individuals with alcoholism). CONCLUSIONS: Our comprehensive analysis of prescription drugs suggests that several marketed drugs inhibit ThTR-2, which may contribute to thiamine deficiency, especially in at-risk populations.

Prescribed Drugs and the Microbiome.

The interaction between drug use, the microbiome, and the host is complex and multidimensional. Drugs and the microbiota may be risk factors or protective factors for disease. These interactions may explain interpersonal variations in drug efficacy and toxicity, but also interpersonal variations in microbiota composition and functioning, and potential (long-term) side effects from drugs.

Drug Interactions in Space: a Cause for Concern?

PURPOSE: Crewmembers aboard the International Space Station (ISS) have free access to an increasing number of medications within medical kits. The aim of the current study was to assess the number, severity and reliability of potential drug-drug interactions (DDIs) involving those medications. METHODS: We evaluated the information obtained from clinical decision support systems. Searches for potential DDIs were applied to published lists of medications available to US astronauts in medical kits aboard the ISS. RESULTS: A total of 311 potential DDIs were identified by Lexi-Interact, of which approximately half were recognized by Micromedex as well. Major, moderate and minor interactions consisted 23.5%, 68.5% and 8.0% of entries, respectively. The reliability of 71.1% of alerts was fair. Commonly used drugs, including zolpidem and zaleplon, were involved in multiple potential interactions that were classified as major based on additive CNS depression. CONCLUSIONS: Most potential DDIs likely to be encountered in space are unestablished even in terrestrial medicine and their assignment is based on class-effects. Yet, some drug combinations may be associated with clinically-relevant consequences. Future DDI rating should be adjusted to space-related outcomes. Until that happens, it would be advisable to avoid non-established drug combinations in space when possible.

A method to account for covariate-specific treatment effects when estimating biomarker associations in the presence of endogenous medication use.

In the modern era, cardiovascular biomarkers are often measured in the presence of medication use, such that the observed biomarker value for the treated participants is different than their underlying natural history value. However, for certain predictors (e.g. age, gender, and genetic exposures) the observed biomarker value is not of primary interest. Rather, we are interested in estimating the association between these predictors and the natural history of the biomarker that would have occurred in the absence of treatment. Nonrandom medication use obscures our ability to estimate this association in cross-sectional observational data. Structural equation methodology (e.g. the treatment effects model), while historically used to estimate treatment effects, has been previously shown to be a reasonable way to correct endogeneity bias when estimating natural biomarker associations. However, the assumption that the effects of medication use on the biomarker are uniform across participants on medication is generally not thought to be reasonable. We derive an extension of the treatment effects model to accommodate effect modification. Based on several simulation studies and an application to data from the Multi-Ethnic Study of Atherosclerosis, we show that our extension substantially improves bias in estimating associations of interest, particularly when effect modifiers are associated with the biomarker or with medication use, without a meaningful cost of efficiency.

Oxidoreductases for the remediation of organic pollutants in water - a critical review.

Water contamination by various recalcitrant organic aromatic compounds is an emerging environmental issue that is increasingly attracting the attention of environmental scientists. A great majority of these recalcitrant pollutants are industrial wastes, textile dyes, pharmaceuticals, hormones, and personal care products that are discharged into wastewater. Not surprisingly, various chemical, physical, and biological strategies have been proposed and developed to remove and/or degrade these pollutants from contaminated water bodies. Biological approaches, specifically using oxidoreductase enzymes (such as peroxidases and laccases) for pollutant degradation are a relatively new and a promising research area that has potential advantages over other methods due to their higher efficiency and the ease of handling. This review focuses on the application of different classes of oxidoreductase enzymes to degrade various classes of organic pollutants. In addition to classifying these enzymes based on structural differences, the major factors that can affect their remediation ability, such as the class of peroxidases employed, pH, molecular structure of the pollutant, temperature, and the presence of redox mediators are also examined and discussed. Interestingly, a literature survey combined with our unpublished data suggests that "peroxidases" are a very heterogeneous and diverse family of enzymes and have different pH profiles, temperature optima, thermal stabilities, requirements for redox mediators, and substrate specificities as well as varying detoxification abilities. Additionally, remediation of real-life polluted samples by oxidoreductases is also highlighted as well as a critical look at current challenges and future perspectives.

Long-term complications arising from bowel interposition in the urinary tract.

After radical cystectomy or pathologies affecting the ureter(s), bowel segments can be employed to restore the natural urine flow or to create an external urinary diversion. Nevertheless, the interposition of bowel segments in the urinary tract is not devoid of complications. In fact, bowel's microstructure differs from the urothelium; specifically its mucosa is aimed at reabsorption, rather than storage. The aim of this paper is to revise the pathophysiology of complications related to bowel's mucosal properties. Those are: metabolic imbalance, malabsorption of vitamins, cholelitiasis, nephrolitiasis and infections. Their entity varies according to the segment used and to its length, which reflects the surface in contact with urine. Mostly, they occur on the long-term, but metabolic imbalances might occur soon after surgery as well.

Metabolic Patterns of Fentanyl, Meperidine, Methylphenidate, Tapentadol and Tramadol Observed in Urine, Serum or Plasma.

Drug testing is a useful tool to identify drug use or monitor adherence to prescription drugs. The interpretation of drug results can be complicated based on the pattern and proportional concentrations of drugs and/or drug metabolite(s). The purpose of this retrospective study was to detect the positivity rates and metabolic patterns of five prescription drugs, including fentanyl, meperidine, methylphenidate, tapentadol and tramadol. Retrospective data were retrieved from the laboratory information system in a national reference laboratory. Drug testing was performed using four mass spectrometry methods that were validated for clinical use. For urine specimens, the positivity rate was the highest for methylphenidate (62.3%, n = 2,489), followed by tramadol (43.7%, n = 3,483), fentanyl (41.9%, n = 4,657), tapentadol (37.9%, n = 736) and meperidine (8.3%, n = 138). Among positive samples, both parent drug and metabolite(s) was detectable in 94.9% of meperidine samples, 94.5% of tramadol samples, 93.8% of fentanyl samples, 89.9% of methylphenidate and 86.6% of tapentadol samples. For serum or plasma specimens, the positivity rate was the highest for tapentadol (75.0%, n = 39), followed by methylphenidate (74.2%, n = 569), fentanyl (53.6%, n = 113), meperidine (41.9%, n = 18) and tramadol (28.9%, n = 213). Similar metabolic patterns were found in serum or plasma. Of positive results, both parent drug and metabolite(s) were found in 94.7% of fentanyl samples, 83.3% of meperidine samples, 79.6% of methylphenidate samples, 53.8% of tapentadol samples and 44.1% of tramadol samples. Our data demonstrates the metabolic patterns of five drugs from a random urine or serum/plasma collection in patients that have been prescribed these medications. The data presented can be used to guide clinicians in determining drug adherence by assessing the positivity rates of the parent drug and corresponding metabolite(s).

Correlation of Subjective Effects with Systemic Opioid Exposure from Fixed-Dose Combinations of Oxycodone/Acetaminophen in Recreational Users of Prescription Drugs.

OBJECTIVE: To correlate abuse-related pharmacodynamic measures and pharmacokinetic measures after administering immediate-release/extended-release and immediate-release oxycodone/acetaminophen fixed-dose combination analgesicsDesign. Randomized, double-blind, active- and placebo-controlled, 7-way crossover studySetting. Contract research organizationSubjects. Nondependent recreational users of prescription opioids. METHODS: Participants received single doses of intact immediate-release/extended-release and immediate-release oxycodone/acetaminophen 15/650 mg, intact immediate-release/extended-release and immediate-release oxycodone/acetaminophen 30/1,300 mg, crushed immediate-release/extended-release and immediate-release oxycodone/acetaminophen 30/1,300 mg, and placebo. Measures of pharmacodynamics (pupillometry, drug liking, drug high, good drug effects) and pharmacokinetics were assessed predose and up to 24 hours postdose, and correlations between pharmacokinetic parameters and pharmacodynamic data were explored. RESULTS: Of 61 participants, 55 completed all 7 treatments. Intact immediate-release/extended-release oxycodone/acetaminophen produced 50% lower oxycodone peak plasma concentration (Cmax) than immediate-release oxycodone/acetaminophen. Median oxycodone time to Cmax (tmax) was significantly longer (P<0.001) for intact immediate-release/extended-release oxycodone/acetaminophen than immediate-release oxycodone/acetaminophen. The pharmacokinetics of crushed immediate-release/extended-release and immediate-release oxycodone/acetaminophen (30/1,300 mg) followed a similar pattern. Crushing did not shorten the median oxycodone tmax for immediate-release/extended-release oxycodone/acetaminophen (30/1,300 mg). Strong correlations were observed between oxycodone Cmax and area under the curve from time 0 to time x peak effects and area under the subjective effect curve from time 0 to time x for all subjective effects (R2=0.711-0.997). CONCLUSION: Immediate-release/extended-release oxycodone/acetaminophen produced lower oxycodone Cmax and longer tmax than immediate-release oxycodone/acetaminophen. Lower oxycodone concentrations, particularly at earlier time points, were strongly correlated with lesser positive subjective drug effects.

Predicting unintended effects of drugs based on off-target tissue effects.

Unintended effects of drugs can be caused by various mechanisms. Conventional analysis of unintended effects has focused on the target proteins of drugs. However, an interaction with off-target tissues of a drug might be one of the unintended effect-related mechanisms. We propose two processes to predict a drug's unintended effects by off-target tissue effects: 1) identification of a drug's off-target tissue and; 2) tissue protein - symptom relation identification (tissue protein - symptom matrix). Using this method, we predicted that 1,177 (10.7%) side-effects were related to off-target tissue effects in 11,041 known side-effects. Off-target tissues and unintended effects of successful repositioning drugs were also predicted. The effectiveness of relations of the proposed tissue protein - symptom matrix were evaluated by using the literature mining method. We predicted unintended effects of drugs as well as those effect-related off-target tissues. By using our prediction, we are able to reduce drug side-effects on off-target tissues and provide a chance to identify new indications of drugs of interest.

Daños causados por la Talidomida. La batalla legal que no cesa. Comentario a la STS de 20 de octubre de 2015 / Damages caused by Thalidomide. A never ending legal battle. Commentary to STS October 20th 2015

Durante los años cincuenta y sesenta se produjo un aumento del número de nacimientos de niños con malformaciones gravísimas, a los que les faltaban los brazos y/o las piernas, consecuencia de la ingestión por parte de sus madres embarazadas de medicamentos con talidomida, para combatir las náuseas y los mareos. Sesenta años después del nacimiento de la llamada «generación de la talidomida», los afectados en España han visto cómo los tribunales han desestimado su reclamación judicial de indemnización. Este artículo hace un repaso al recorrido judicial de la reclamación en España, y lo compara con la respuesta legal que ha recibido en otros países (AU)

During the fifties and sixties there was a huge increase in the number of children born with very serious malformations, who lacked legs and/or arms as a consequence of their mothers’ having taken medicines with thalidomide, marketed against nausea and dizziness. Sixty years after the «thalidomide generation» was born, the Spanish victims have seen how courts have rejected their claim. This paper will analyze the Spanish legal journey and will compare it to the legal response in other countries (AU)

Commercial insulin immunoassays fail to detect commonly prescribed insulin analogues.

OBJECTIVES: Blood insulin and C-peptide are key investigations in the differential diagnosis of hypoglycaemia. Analogues of insulin have modified primary-sequences compared to native human insulin, as such may not cross react with insulin assays. This has important implications in detecting surreptitious or malicious insulin administration. The aim of this study is to assess the cross-reactivity of all insulins currently listed in the British National Formulary (BNF65, 2013) in clinical insulin assays currently used in UK clinical laboratories. DESIGN AND METHODS: Sample sets were prepared for all 15 exogenous insulin classes listed in the BNF, at concentrations of 1000 pmol/L and 300 pmol/L, using pooled human serum. Samples were sent blinded to 5 participating analytical laboratories to cover analysis on the 10 major clinical insulin assays used in the UK. RESULTS: The ability of insulin assays to detect exogenous insulin preparations was highly variable and ranged from 0% to >140% for a single exogenous insulin. Four assays were highly specific for the human insulin sequence and had no cross-reactivity with any synthetic analogue insulin. Two detected all insulin types (human sequence, animal and synthetic analogue), with the remaining having variable cross-reactivity. CONCLUSION: The cross-reactivity of the 15 exogenous insulin preparations is highly variable in the assays used in clinical laboratories around the UK. It is important that laboratories and clinicians are aware of the limitations of their local assays to avoid missing the important diagnosis of hypoglycaemia secondary to excessive exogenous insulin. Where necessary, samples should be referred to specialist centres for insulin analysis and ideally by a validated and fully-quantitative mass spectrometry-based method.

Intestinal and hepatic drug transporters: pharmacokinetic, pathophysiological, and pharmacogenetic roles.

The efficacy and safety of pharmacotherapies are determined by the complex processes involved in the interactions between drugs with the human body, including pharmacokinetic aspects. Among pharmacokinetic factors, it has been recognized that drug transporters play critical roles for absorption, distribution and excretion of drugs, regulating the membrane transport of drugs. The vast amounts of information on drug transporters collected in the past 20 years have been organized according to biochemical, molecular, genetic, and clinical analyses. Novel technologies, public databases, and regulatory guidelines have advanced the use of such information in drug development and clinical practice. In this review, we selected some clinically important drug transporters expressed in the intestine and liver, and introduced the research history and current knowledge of their pharmacokinetic, pathophysiological, and pharmacogenetic implications.

Contribution of metabolites to P450 inhibition-based drug-drug interactions: scholarship from the drug metabolism leadership group of the innovation and quality consortium metabolite group.

Recent European Medicines Agency (final) and US Food and Drug Administration (draft) drug interaction guidances proposed that human circulating metabolites should be investigated in vitro for their drug-drug interaction (DDI) potential if present at ≥ 25% of the parent area under the time-concentration curve (AUC) (US Food and Drug Administration) or ≥ 25% of the parent and ≥ 10% of the total drug-related AUC (European Medicines Agency). To examine the application of these regulatory recommendations, a group of scientists, representing 18 pharmaceutical companies of the Drug Metabolism Leadership Group of the Innovation and Quality Consortium, conducted a scholarship to assess the risk of contributions by metabolites to cytochrome P450 (P450) inhibition-based DDIs. The group assessed the risk of having a metabolite as the sole contributor to DDI based on literature data and analysis of the 137 most frequently prescribed drugs, defined structural alerts associated with P450 inhibition/inactivation by metabolites, and analyzed current approaches to trigger in vitro DDI studies for metabolites. The group concluded that the risk of P450 inhibition caused by a metabolite alone is low. Only metabolites from 5 of 137 drugs were likely the sole contributor to the in vivo P450 inhibition-based DDIs. Two recommendations were provided when assessing the need to conduct in vitro P450 inhibition studies for metabolites: 1) consider structural alerts that suggest P450 inhibition potential, and 2) use multiple approaches (e.g., a metabolite cut-off value of 100% of the parent AUC and the R(met) strategy) to predict P450 inhibition-based DDIs caused by metabolites in the clinic.