Drug Metabolism Reviews最新文献

{"title":"Pharmacogenomics in the era of next generation sequencing - from byte to bedside.","authors":"Laura E Russell,&nbsp;Yitian Zhou,&nbsp;Ahmed A Almousa,&nbsp;Jasleen K Sodhi,&nbsp;Chukwunonso K Nwabufo,&nbsp;Volker M Lauschke","doi":"10.1080/03602532.2021.1909613","DOIUrl":"https://doi.org/10.1080/03602532.2021.1909613","url":null,"abstract":"<p><p>Pharmacogenetic research has resulted in the identification of a multitude of genetic variants that impact drug response or toxicity. These polymorphisms are mostly common and have been included as actionable information in the labels of numerous drugs. In addition to common variants, recent advances in Next Generation Sequencing (NGS) technologies have resulted in the identification of a plethora of rare and population-specific pharmacogenetic variations with unclear functional consequences that are not accessible by conventional forward genetics strategies. In this review, we discuss how comprehensive sequencing information can be translated into personalized pharmacogenomic advice in the age of NGS. Specifically, we provide an update of the functional impacts of rare pharmacogenetic variability and how this information can be leveraged to improve pharmacogenetic guidance. Furthermore, we critically discuss the current status of implementation of pharmacogenetic testing across drug development and layers of care. We identify major gaps and provide perspectives on how these can be minimized to optimize the utilization of NGS data for personalized clinical decision-support.</p>","PeriodicalId":11307,"journal":{"name":"Drug Metabolism Reviews","volume":"53 2","pages":"253-278"},"PeriodicalIF":5.9,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/03602532.2021.1909613","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25562878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toxicogenomics of drug induced liver injury - from mechanistic understanding to early prediction.","authors":"Volker M Lauschke","doi":"10.1080/03602532.2021.1894571","DOIUrl":"https://doi.org/10.1080/03602532.2021.1894571","url":null,"abstract":"<p><p>Despite rigorous preclinical testing, clinical attrition rates in drug development remain high with drug-induced liver injury (DILI) remaining one of the most frequent causes of project failures. To understand DILI mechanisms, major efforts are put into the development of physiologically relevant cell models and culture paradigms with the aim to enhance preclinical to clinical result translation. While the majority of toxicogenomic studies have been based on cell lines, there are emerging trends toward the predominant use of stem cell-derived organoids and primary human hepatocytes in complex 3D cell models. Such studies have been successful in disentangling diverse toxicity mechanisms, including genotoxicity, mitochondrial injury, steatogenesis and cholestasis and can aid in distinguishing hepatotoxic from nontoxic structural analogs. Furthermore, by leveraging inter-individual differences of cells from different donors, these approaches can emulate the complexity of polygenic risk scores, which facilitates personalized drug-specific DILI risk analyses. In summary, toxicogenomic studies into drug-induced hepatotoxicity have majorly contributed to our mechanistic understanding of DILI and the incorporation of organotypic human 3D liver models into the preclinical testing arsenal promises to enhance biological insights during drug discovery, increase confidence in preclinical safety and minimize the translational gap.</p>","PeriodicalId":11307,"journal":{"name":"Drug Metabolism Reviews","volume":"53 2","pages":"245-252"},"PeriodicalIF":5.9,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/03602532.2021.1894571","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25459541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"International Society for the Study of Xenobiotics (ISSX) New Investigator Group Committee 2019-2020 concluding remarks.","authors":"Mary A Schleiff,&nbsp;Jasleen K Sodhi","doi":"10.1080/03602532.2021.1910293","DOIUrl":"https://doi.org/10.1080/03602532.2021.1910293","url":null,"abstract":"<p><p>The International Society for the Study of Xenobiotics (ISSX) New Investigators Group has assembled a global team of emerging scientists to collaboratively compose a series of articles whose topics span the broad field of drug metabolism and will guide both new and established investigators alike. The New Investigator Group Committee members are proud to have provided such an opportunity to many promising early-career scientists from across the globe, and would like to acknowledge each contributor for their efforts.</p>","PeriodicalId":11307,"journal":{"name":"Drug Metabolism Reviews","volume":"53 2","pages":"279-284"},"PeriodicalIF":5.9,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/03602532.2021.1910293","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25576776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of drug-induced kidney injury in drug discovery.","authors":"Priyanka Kulkarni","doi":"10.1080/03602532.2021.1922436","DOIUrl":"https://doi.org/10.1080/03602532.2021.1922436","url":null,"abstract":"<p><p>Drug induced kidney injury is one of the leading causes of failure of drug development programs in the clinic. Early prediction of renal toxicity potential of drugs is crucial to the success of drug candidates in the clinic. The dynamic nature of the functioning of the kidney and the presence of drug uptake proteins introduce additional challenges in the prediction of renal injury caused by drugs. Renal injury due to drugs can be caused by a wide variety of mechanisms and can be broadly classified as toxic or obstructive. Several biomarkers are available for <i>in vitro</i> and in vivo detection of renal injury. <i>In vitro</i> static and dynamic (microfluidic) cellular models and preclinical models can provide valuable information regarding the toxicity potential of drugs. Differences in pharmacology and subsequent disconnect in biomarker response, differences in the expression of transporter and enzyme proteins between <i>in vitro</i> to in vivo systems and between preclinical species and humans are some of the limitations of current experimental models. The progress in microfluidic (kidney-on-chip) platforms in combination with the ability of 3-dimensional cell culture can help in addressing some of these issues in the future. Finally, newer in silico and computational techniques like physiologically based pharmacokinetic modeling and machine learning have demonstrated potential in assisting prediction of drug induced kidney injury.</p>","PeriodicalId":11307,"journal":{"name":"Drug Metabolism Reviews","volume":"53 2","pages":"234-244"},"PeriodicalIF":5.9,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/03602532.2021.1922436","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38992147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introduction to the mini special issue on next generation drug discovery and development: rethinking translational pharmacology for accelerated drug development.","authors":"Chukwunonso K Nwabufo","doi":"10.1080/03602532.2021.1909614","DOIUrl":"https://doi.org/10.1080/03602532.2021.1909614","url":null,"abstract":"<p><p>The coronavirus disease (COVID-19) pandemic further revealed the barriers to accelerated discovery and development of transformative medicines for life threatening diseases. To effectively and efficiently respond to unmet medical needs, efforts should be directed towards revolutionizing the predictive capability of non-clinical surrogates that inform drug discovery and development programs. I developed this mini special issue amidst the COVID-19 pandemic to evaluate recent advancements and opportunities for four main subthemes that support drug discovery and development including prediction of metabolic pathways, translational pharmacokinetic and pharmacodynamic studies, pharmacogenomics, and trends in bioanalysis. Scientific papers in these areas were covered by investigators from the International Society for the Study of Xenobiotics New Investigator Group and other investigators. Advancement in the predictive capability of in silico, <i>in vitro</i>, and <i>in vivo</i> models used to determine the absorption, distribution, metabolism, excretion, and toxicity profile of investigational drugs can help offset the cost of unexpected safety and/or efficacy issues during clinical studies. Likewise, extensive application of pharmacogenomics in drug development and clinical care can help direct therapeutic benefits to the appropriate patient population with the overall goal of accelerating drug development and mitigating failed drug cost. Finally, I hope that the scientific contributions in this mini special issue will stimulate practical advancements across all aspects of basic science research that support drug discovery and development to help unlock the door to the next generation of drug discovery and development that features reduced failure rates and accelerated development.</p>","PeriodicalId":11307,"journal":{"name":"Drug Metabolism Reviews","volume":"53 2","pages":"171-172"},"PeriodicalIF":5.9,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/03602532.2021.1909614","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38960858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent developments in predicting CYP-independent metabolism.","authors":"Nikhilesh V Dhuria,&nbsp;Bianka Haro,&nbsp;Amit Kapadia,&nbsp;Khadjia A Lobo,&nbsp;Bernice Matusow,&nbsp;Mary A Schleiff,&nbsp;Christina Tantoy,&nbsp;Jasleen K Sodhi","doi":"10.1080/03602532.2021.1923728","DOIUrl":"https://doi.org/10.1080/03602532.2021.1923728","url":null,"abstract":"<p><p>As lead optimization efforts have successfully reduced metabolic liabilities due to cytochrome P450 (CYP)-mediated metabolism, there has been an increase in the frequency of involvement of non-CYP enzymes in the metabolism of investigational compounds. Although there have been numerous notable advancements in the characterization of non-CYP enzymes with respect to their localization, reaction mechanisms, species differences and identification of typical substrates, accurate prediction of non-CYP-mediated clearance, with a particular emphasis with the difficulties in accounting for any extrahepatic contributions, remains a challenge. The current manuscript comprehensively summarizes the recent advancements in the prediction of drug metabolism and the <i>in vitro</i> to <i>in vitro</i> extrapolation of clearance for substrates of non-CYP drug metabolizing enzymes.</p>","PeriodicalId":11307,"journal":{"name":"Drug Metabolism Reviews","volume":"53 2","pages":"188-206"},"PeriodicalIF":5.9,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/03602532.2021.1923728","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38943956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent developments in <i>in vitro</i> and <i>in vivo</i> models for improved translation of preclinical pharmacokinetics and pharmacodynamics data.","authors":"Jaydeep Yadav,&nbsp;Mehdi El Hassani,&nbsp;Jasleen Sodhi,&nbsp;Volker M Lauschke,&nbsp;Jessica H Hartman,&nbsp;Laura E Russell","doi":"10.1080/03602532.2021.1922435","DOIUrl":"https://doi.org/10.1080/03602532.2021.1922435","url":null,"abstract":"<p><p>Improved pharmacokinetics/pharmacodynamics (PK/PD) prediction in the early stages of drug development is essential to inform lead optimization strategies and reduce attrition rates. Recently, there have been significant advancements in the development of new <i>in vitro</i> and <i>in vivo</i> strategies to better characterize pharmacokinetic properties and efficacy of drug leads. Herein, we review advances in experimental and mathematical models for clearance predictions, advancements in developing novel tools to capture slowly metabolized drugs, <i>in vivo</i> model developments to capture human etiology for supporting drug development, limitations and gaps in these efforts, and a perspective on the future in the field.</p>","PeriodicalId":11307,"journal":{"name":"Drug Metabolism Reviews","volume":"53 2","pages":"207-233"},"PeriodicalIF":5.9,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/03602532.2021.1922435","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38993554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent advances in computational metabolite structure predictions and altered metabolic pathways assessment to inform drug development processes.","authors":"Mary Alexandra Schleiff,&nbsp;Deepika Dhaware,&nbsp;Jasleen K Sodhi","doi":"10.1080/03602532.2021.1910292","DOIUrl":"https://doi.org/10.1080/03602532.2021.1910292","url":null,"abstract":"<p><p>Many drug candidates fail during preclinical and clinical trials due to variable or unexpected metabolism which may lead to variability in drug efficacy or adverse drug reactions. The drug metabolism field aims to address this important issue from many angles which range from the study of drug-drug interactions, pharmacogenomics, computational metabolic modeling, and others. This manuscript aims to provide brief but comprehensive manuscript summaries highlighting the conclusions and scientific importance of seven exceptional manuscripts published in recent years within the field of drug metabolism. Two main topics within the field are reviewed: novel computational metabolic modeling approaches which provide complex outputs beyond site of metabolism predictions, and experimental approaches designed to discern the impacts of interindividual variability and species differences on drug metabolism. The computational approaches discussed provide novel outputs in metabolite structure and formation likelihood and/or extend beyond the saturated field of drug phase I metabolism, while the experimental metabolic pathways assessments aim to highlight the impacts of genetic polymorphisms and clinical animal model metabolic differences on human metabolism and subsequent health outcomes.</p>","PeriodicalId":11307,"journal":{"name":"Drug Metabolism Reviews","volume":"53 2","pages":"173-187"},"PeriodicalIF":5.9,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/03602532.2021.1910292","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25581116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ibuprofen-based advanced therapeutics: breaking the inflammatory link in cancer, neurodegeneration, and diseases.","authors":"Arun Upadhyay,&nbsp;Ayeman Amanullah,&nbsp;Vibhuti Joshi,&nbsp;Rohan Dhiman,&nbsp;Vijay Kumar Prajapati,&nbsp;Krishna Mohan Poluri,&nbsp;Amit Mishra","doi":"10.1080/03602532.2021.1903488","DOIUrl":"https://doi.org/10.1080/03602532.2021.1903488","url":null,"abstract":"<p><p>Ibuprofen is a classical nonsteroidal anti-inflammatory drug (NSAID) highly prescribed to reduce acute pain and inflammation under an array of conditions, including rheumatoid arthritis, osteoarthritis, dysmenorrhea, and gout. Ibuprofen acts as a potential inhibitor for cyclooxygenase enzymes (COX-1 and COX-2). In the past few decades, research on this small molecule has led to identifying other possible therapeutic benefits. Anti-tumorigenic and neuroprotective functions of Ibuprofen are majorly recognized in recent literature and need further consideration. Additionally, several other roles of this anti-inflammatory molecule have been discovered and subjected to experimental assessment in various diseases. However, the major challenge faced by Ibuprofen and other drugs of similar classes is their side effects, and tendency to cause gastrointestinal injury, generate cardiovascular risks, modulate hepatic and acute kidney diseases. Future research should also be conducted to deduce new methods and approaches of suppressing the unwanted toxic changes mediated by these drugs and develop new therapeutic avenues so that these small molecules continue to serve the purposes. This article primarily aims to develop a comprehensive and better understanding of Ibuprofen, its pharmacological features, therapeutic benefits, and possible but less understood medicinal properties apart from major challenges in its future application.KEY POINTSIbuprofen, an NSAID, is a classical anti-inflammatory therapeutic agent.Pro-apoptotic roles of NSAIDs have been explored in detail in the past, holding the key in anti-cancer therapies.Excessive and continuous use of NSAIDs may have several side effects and multiple organ damage.Hyperactivated Inflammation initiates multifold detrimental changes in multiple pathological conditions.Targeting inflammatory pathways hold the key to several therapeutic strategies against many diseases, including cancer, microbial infections, multiple sclerosis, and many other brain diseases.</p>","PeriodicalId":11307,"journal":{"name":"Drug Metabolism Reviews","volume":"53 1","pages":"100-121"},"PeriodicalIF":5.9,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/03602532.2021.1903488","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25562879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Molecular modeling approaches to address drug-metabolizing enzymes (DMEs) mediated chemoresistance: a review.","authors":"Baddipadige Raju,&nbsp;Shalki Choudhary,&nbsp;Gera Narendra,&nbsp;Himanshu Verma,&nbsp;Om Silakari","doi":"10.1080/03602532.2021.1874406","DOIUrl":"https://doi.org/10.1080/03602532.2021.1874406","url":null,"abstract":"<p><p>Resistance against clinically approved anticancer drugs is the main roadblock in cancer treatment. Drug metabolizing enzymes (DMEs) that are capable of metabolizing a variety of xenobiotic get overexpressed in malignant cells, therefore, catalyzing drug inactivation. As evident from the literature reports, the levels of DMEs increase in cancer cells that ultimately lead to drug inactivation followed by drug resistance. To puzzle out this issue, several strategies inclusive of analog designing, prodrug designing, and inhibitor designing have been forged. On that front, the implementation of computational tools can be considered a fascinating approach to address the problem of chemoresistance. Various research groups have adopted different molecular modeling tools for the investigation of DMEs mediated toxicity problems. However, the utilization of these <i>in-silico</i> tools in maneuvering the DME mediated chemoresistance is least considered and yet to be explored. These tools can be employed in the designing of such chemotherapeutic agents that are devoid of the resistance problem. The current review canvasses various molecular modeling approaches that can be implemented to address this issue. Special focus was laid on the development of specific inhibitors of DMEs. Additionally, the strategies to bypass the DMEs mediated drug metabolism were also contemplated in this report that includes analogs and pro-drugs designing. Different strategies discussed in the review will be beneficial in designing novel chemotherapeutic agents that depreciate the resistance problem.</p>","PeriodicalId":11307,"journal":{"name":"Drug Metabolism Reviews","volume":"53 1","pages":"45-75"},"PeriodicalIF":5.9,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/03602532.2021.1874406","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25327681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}