Variability In Drug Response Research Articles

The Bidirectional Relationship Between Cardiovascular Medications and Oral and Gut Microbiome Health: A Comprehensive Review

Cardiovascular diseases (CVDs) are a leading cause of widespread morbidity and mortality. It has been found that the gut and oral microbiomes differ in individuals with CVDs compared to healthy individuals. Patients with CVDs often require long-term pharmacological interventions. While these medications have been extensively studied for their cardiovascular benefits, emerging research indicates that they may also impact the diversity and composition of the oral and gut microbiomes. However, our understanding of how these factors influence the compositions of the oral and gut microbiomes in individuals remains limited. Studies have shown that statins and beta-blockers, in particular, cause gut and oral microbial dysbiosis, impacting the metabolism and absorption of these medications. These alterations can lead to variations in drug responses, highlighting the need for personalized treatment approaches. The microbiome’s role in drug metabolism and the impact of CVD medications on the microbiome are crucial in understanding these variations. However, there are very few studies in this area, and not all medications have been studied, emphasizing the necessity for further research to conclusively establish cause-and-effect relationships and determine the clinical significance of these interactions. This review will provide evidence of how the oral and gut microbiomes in patients with cardiovascular diseases (CVDs) interact with specific drugs used in CVD treatment.

Comparative genomics of Plasmodium yoelii nigeriensis N67 and N67C: genome-wide polymorphisms, differential gene expression, and drug resistance

BackgroundThe study of rodent malaria parasites has significantly advanced our understanding of malaria parasite biology and host responses to parasite infections. There are four well-characterized rodent malaria parasite species (Plasmodium yoelii, P. chabaudi, P. berghei, and P. vinckei). Each species also has multiple strains that cause different disease phenotypes. P. yoelii nigeriensis N67C and N67, two isogenic parasites, are particularly intriguing as they differ in virulence and incite different immune responses in mice. The genome of the N67 parasite has been assembled recently, but not that of N67C. This study used PacBio HiFi sequencing data to assemble the N67C genome, compared the two genomes, and performed RNA sequencing to identify polymorphisms and differentially expressed genes (DEGs).ResultsThe assembled N67C parasite genome consisted of 16 scaffolds and three contigs of approximately 22.5 Mb with 100% and 96.6% completeness based on well-characterized single-copy orthologs specific to the Apicomplexa phylum and the Plasmodium genus, respectively. A comparison between the annotated N67C and N67 genomes revealed 133 single nucleotide polymorphisms (SNPs) and 75 indels. Among the polymorphic sites, an S (N67) to N (N67C) amino acid substitution at position 114 (S114N) in the dihydrofolate reductase-thymidylate synthase (DHFR-TS) confers resistance to pyrimethamine in mice. Additionally, 60 differentially expressed single-copy genes (DEGs) were detected after comparing mRNA levels between the two parasites. Starting with the predicted and annotated 5,681 N67C and 5,749 N67 genes, we identified 4,641 orthogroups that included at least one gene from the four P. yoelii parasites (N67, N67C, 17X, and YM), whereas 758 orthogroups showed subspecies or strain-specific patterns.ConclusionThe identification of polymorphic sites between the N67 and N67C genomes, along with the detection of the DEGs, may provide crucial insights into the variations in parasite drug responses and disease severity between these two isogenic parasites. The functional characterization of these genetic differences and candidate genes will deepen our understanding of disease mechanisms and pave the way for developing more effective control measures against malaria.

Drug Metabolizing Enzymes: An Exclusive Guide into Latest Research in Pharmaco-genetic Dynamics in Arab Countries.

Drug metabolizing enzymes play a crucial role in the pharmacokinetics and pharmacodynamics of therapeutic drugs, influencing their efficacy and safety. This review explores the impact of genetic polymorphisms in drug-metabolizing genes on drug response within Arab populations. We examine the genetic diversity specific to Arab countries, focusing on the variations in key drug-metabolizing enzymes such as CYP450, GST, and UGT families. The review highlights recent research on polymorphisms in these genes and their implications for drug metabolism, including variations in allele frequencies and their effects on therapeutic outcomes. Additionally, the paper discusses how these genetic variations contribute to the variability in drug response and adverse drug reactions among individuals in Arab populations. By synthesizing current findings, this review aims to provide a comprehensive understanding of the pharmacogenetic landscape in Arab countries and offer insights into personalized medicine approaches tailored to genetic profiles. The findings underscore the importance of incorporating pharmacogenetic data into clinical practice to enhance drug efficacy and minimize adverse effects, ultimately paving the way for more effective and individualized treatment strategies in the region.

Longitudinal assessment of SNPs rs72552763 and rs622342 in SLC22A1 over HbA1c control among Mexican-Mestizo diabetic type 2 patients.

Background: In Mexico, 75% of diabetes mellitus type 2 (DMT2) patients are not in glycaemic control criteria (HbA1c<7%); this entails a significantly variable drug response. Amongst the factors influencing such variability, are genetics, more specifically, single nucleotide polymorphisms (SNPs). Three genes implied in metformin pharmacokinetics are SLC22A1, SLC22A2, and SLC22A3, which are polymorphic. While there have been cross-sectional studies on their SNPs impact over drug response, a longitudinal study would contribute valuable information on their effect over time. Methods: SNPs of SLC22A1 (rs72552763, rs622342, rs12208357, rs2282143, rs594709, rs628031, and rs683369), SLC22A2 (rs316019), and SLC22A3 (rs2076828), were determined through PCR-TR. The clinical records of 69 patients undergoing metformin monotherapy were retrospectively assessed. Metformin is the first line treatment against DMT2. A level of HbA1c <7% (time 0) was considered as an inescapable inclusion criterion. The study's cases were those patients who reported HbA1c ≥ 7% (time1) after time 0 (t0). Kaplan-Meier curves including a Log-Rank test and a Cox multivariate analysis of proportional risks were performed. Aim: Determining clinical, biochemical, and genetic variables which may affect non-control (HbA1c ≥ 7%) survival time spans amongst DMT2 Mexican-Mestizo patients undergoing metformin monotherapy at Hospital Regional de Alta Especialidad de Ixtapaluca (HRAEI) between October 2013 and December 2023. Results: All 69 patients were monitored over a median period of 642 days (273-1,134). A comparison between time 0 and time 1 (t1) revealed differences in weight (p = 0.036), metformin dose mg/kg/day (p = 0.003), plasmatic glucose mg/dL (p = 0.048), and HbA1c (p < 0.001). The median non-control survival rate was different across the 3 genotypes of rs62552763 in SLC22A1 (p = 0.0034) and the dominant genotypic model GAT/GAT vs. GAT/del + del/del (p = 0.009). There were differences between rs622342 genotypes as well (p = 0.041). In GAT/GAT the Cox model found HR = 0.407 (IC95%: 0.202-0.818, p = 0.011) in the univariate analysis and HR = 0.418 (IC95%: 0.204-0.856, p = 0.034) in the multivariate analysis, adjusted by initial metformin dose (mg/kg/day), initial weight (kg), and final metformin dose (mg/kg/day). Genotype A/A of rs622342 in SLC22A1, reported HR = 0.392 (IC95%: 0.169-0.910, p = 0.029) in the multivariate analysis as well. Conclusion: Among DMT2 Mexican-Mestizo patients undergoing metformin monotherapy the minor allele del in rs72552763 and the minor allele C in rs622342 reported a significantly shorter survival median respect to the wild type variant. Patients carrying del in rs72552763 or C in rs622342, both in SLC22A1, will reach non-control in less time with respect to other patients. Therefore these genotypes may constitute a therapeutic response biomarker for this population.

Pharmacometabolomics of sulfonylureas in patients with type 2 diabetes: a cross-sectional study.

Sulfonylureas have been a longstanding pharmacotherapy in the management of type 2 diabetes, with potential benefits beyond glycemic control. Although sulfonylureas are effective, interindividual variability exists in drug response. Pharmacometabolomics is a potent method for elucidating variations in individual drug response. Identifying unique metabolites associated with treatment response can improve our ability to predict outcomes and optimize treatment strategies for individual patients. Our objective is to identify metabolic signatures associated with good and poor response to sulfonylureas, which could enhance our capability to anticipate treatment outcome. In this cross-sectional study, clinical and metabolomics data for 137 patients with type 2 diabetes who are taking sulfonylurea as a monotherapy or a combination therapy were obtained from Qatar Biobank. Patients were empirically categorized according to their glycosylated hemoglobin levels into poor and good responders to sulfonylureas. To examine variations in metabolic signatures between the two distinct groups, we have employed orthogonal partial least squares discriminant analysis and linear models while correcting for demographic confounders and metformin usage. Good responders showed increased levels of acylcholines, gamma glutamyl amino acids, sphingomyelins, methionine, and a novel metabolite 6-bromotryptophan. Conversely, poor responders showed increased levels of metabolites of glucose metabolism and branched chain amino acid metabolites. The results of this study have the potential to empower our knowledge of variability in patient response to sulfonylureas, and carry significant implications for advancing precision medicine in type 2 diabetes management.

Development and validation of a prognostic signature of breast cancer based on drug absorption, distribution, metabolism and excretion (ADME)-related genes

The individual variation of carcinogenesis and drug response is influenced by the absorption, distribution, metabolism, and excretion (ADME) of drugs. The utilization of signatures derived from ADME-related genes holds potential for predicting prognosis and treatment response across diverse cancer types. Further investigation is required to completely understand the role of ADME-associated genes in breast cancer. A signature was constructed through the application of a least absolute shrinkage and selection operator regression model, employing prognostic differentially expressed genes found in both cancer tissue and normal tissue. To assess the robustness of the signature, verification analyses were carried out. RT-qPCR was utilized for the validation of gene expression related to risk. Subsequently, a nomogram was developed to enhance the clinical utility of our prognostic tool. The ADME signature, comprising four genes, was established and exhibited a robust association with the prognoses of individuals diagnosed with breast cancer. The nomogram was created by fusing the clinicopathological characteristics with the ADME signature. The ADME signature demonstrated remarkable superiority when compared to the performance of the other individual predictors. Additionally, the analysis of the immune microenvironment revealed that the ImmuneScores of the low-risk group were elevated. The variation in both the infiltration of immune cells and the expression of immune-related genes in the tissues differed among the two groups. For patients with breast cancer, the utilization of ADME signatures as biomarkers presents a significant reference point for prognosis and individualized treatment strategies.

Sex-dependent metabolic remodeling of kidneys revealed by arteriovenous metabolomics.

Sex is a fundamental biological variable important in biomedical research, drug development, clinical trials, and prevention approaches. Among many organs, kidneys are known to exhibit remarkable structural, histological, and pathological differences between sexes. However, whether and how kidneys display distinct metabolic activities between sexes is poorly understood. By developing kidney-specific arteriovenous (AV) metabolomics combined with transcriptomics, we report striking sex differences in both basal metabolic activities and adaptive metabolic remodeling of kidneys after a fat-enriched ketogenic diet (KD), a regimen known to mitigate kidney diseases and improve immunotherapy for renal cancer. At the basal state, female kidneys show highly accumulated aldosterone and various acylcarnitines. In response to the KD, aldosterone levels remain high selectively in females but the sex difference in acylcarnitines disappears. AV data revealed that, under KD, female kidneys avidly take up circulating fatty acids and release 3-hydroxybutyrate (3-HB) whereas male kidneys barely absorb fatty acids but consistently take up 3-HB. Although both male and female kidneys take up gluconeogenic substrates such as glycerol, glutamine and lactate, only female kidneys exhibit net glucose release. Kidney transcriptomics data incompletely predict these sex differences, suggesting post-transcriptional/translational regulation mechanisms. This study provides foundational insights into the sex-dependent and diet-elicited metabolic flexibility of the kidneys in vivo, serving as a unique resource for understanding variable disease prevalence and drug responses between male and female kidneys.

Prevalence of actionable pharmacogenetic variants and high-risk drug prescriptions: A Swiss hospital-based cohort study.

Drug type and dosing recommendation have been designed and optimized based on average response in the general population. Yet, there is significant inter-individual variability in drug response, which results in treatment inefficacy or adverse drug reactions in a subset of patients. This is partly due to genetic factors that typically affect drug metabolism or clearance. To verify the relevance and applicability of international pharmacogenetic guidelines in the Swiss population, we genotyped 1533 patients from a hospital-based biobank who received at least 30 different drugs, as documented in their electronic health record. We then assessed the prevalence of clinically actionable variants in 13 high-risk pharmacogenes. We compared the allele frequencies obtained in the hospital-based cohort with those of a Swiss population-based cohort of 4791 individuals. The prevalence of clinically actionable variants was comparable between the two cohorts, with most study participants (97.3%) carrying at least one actionable pharmacogenetic variant. We then assessed the frequency of high-risk prescriptions due to actionable gene-drug interactions and observed that 31% of patients in the hospital-based cohort were prescribed at least one drug for which they carried a high-risk variant, and for which international guidelines recommend a change of drug or dosage. Our analysis confirms the high prevalence of actionable pharmacogenetic variants in the Swiss population. It also shows that a substantial minority of patients are exposed to drugs for which they carry potentially problematic variants. Implementing a genetically informed approach to drug prescribing could have a positive impact on the quality of healthcare delivery.

Mechanistic modeling of cell viability assays with in silico lineage tracing.

Data from cell viability assays, which measure cumulative division and death events in a population and reflect substantial cellular heterogeneity, are widely available. However, interpreting such data with mechanistic computational models is hindered because direct model/data comparison is often muddled. We developed an algorithm that tracks simulated division and death events in mechanistically detailed single-cell lineages to enable such a model/data comparison and suggest causes of cell-cell drug response variability. Using our previously developed model of mammalian single-cell proliferation and death signaling, we simulated drug dose response experiments for four targeted anti-cancer drugs (alpelisib, neratinib, trametinib and palbociclib) and compared them to experimental data. Simulations are consistent with data for strong growth inhibition by trametinib (MEK inhibitor) and overall lack of efficacy for alpelisib (PI-3K inhibitor), but are inconsistent with data for palbociclib (CDK4/6 inhibitor) and neratinib (EGFR inhibitor). Model/data inconsistencies suggest (i) the importance of CDK4/6 for driving the cell cycle may be overestimated, and (ii) that the cellular balance between basal (tonic) and ligand-induced signaling is a critical determinant of receptor inhibitor response. Simulations show subpopulations of rapidly and slowly dividing cells in both control and drug-treated conditions. Variations in mother cells prior to drug treatment all impinging on ERK pathway activity are associated with the rapidly dividing phenotype and trametinib resistance. This work lays a foundation for the application of mechanistic modeling to large-scale cell viability assay datasets and better understanding determinants of cellular heterogeneity in drug response.

Pharmacogenetics of angiotensin-converting enzyme inhibitors (ACEI) and angiotensin II receptor blockers (ARB) in cardiovascular diseases

Cardiovascular diseases (CVDs) have a high mortality rate, and despite the several available therapeutic targets, non-response to antihypertensives remains a common problem. Angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) are important classes of drugs recommended as first-line therapy for several CVDs. However, response to ACEIs and ARBs varies among treated patients. Pharmacogenomics assesses how an individual's genetic characteristics affect their likely response to drug therapy. Currently, numerous studies suggest that genetic polymorphisms may contribute to variability in drug response. Moreover, further studies evaluating gene-gene interactions within signaling pathways in response to antihypertensives might help to unravel potential genetic predictors for antihypertensive response. This review summarizes the pharmacogenetic data for ACEIs and ARBs in patients with CVD, and discusses the potential pharmacogenetics of these classes of antihypertensives in clinical practice. However, replication studies in different populations are needed. In addition, studies that evaluate gene-gene interactions that share signaling pathways in the response to antihypertensive drugs might facilitate the discovery of genetic predictors for antihypertensive response.

(Invited) Cardiac Engineering: Optical Mapping in Cellular Communication

Microfluidic platforms have emerged as valuable tools for replicating the adverse effects of chemotherapy drugs on cardiac and non-cardiac cells in vitro. However, these systems currently fall short of comprehensively capturing the intricate nature of human responses to chemotherapy, necessitating further validation to enhance model accuracy. While innovative chip systems provide insightful means for screening cardiac toxicity, they encounter challenges in addressing individual variations in drug responses and the influence of patient-specific factors.A novel cardiac chip incorporating intracellular electrophysiological techniques was developed to monitor acute hypoxia-induced responses in cardiac cells. Despite its promising potential, the model exhibits limitations inherent to its simplified two-dimensional microfluidic system, warranting additional validation and optimization efforts to enhance physiological relevance and facilitate applications in clinical translation.Moreover, previous research has proposed the formation of functional connections between human neurons and myocardial cells in a microfluidic chip. However, this study primarily focused on one-way connections between neurons and myocardial cells, overlooking the complexities of bidirectional neuro-muscular connections. Consequently, further research is imperative to deepen our understanding of the intricate interactions between neurons and myocardial cells.Optical mapping techniques assessed transmembrane voltage, mitochondrial activity, and calcium signals during electric pacing. These comprehensive assessments aim to advance our understanding of cardiac electrophysiology under drug exposure, providing crucial insights into the intricate responses of cardiac cells to chemotherapy. This research has significant implications for enhancing our understanding of cardiac safety in the context of chemotherapy.

Size and shape control of microgel-encapsulating tumor spheroid via a user-friendly solenoid valve-based sorter and its application on precise drug testing

The precision of previous cancer research based on tumor spheroids, especially the microgel-encapsulating tumor spheroids, was limited by the high heterogeneity in the tumor spheroid size and shape. Here, we reported a user-friendly solenoid valve-based sorter to reduce this heterogeneity. The artificial intelligence algorithm was employed to detect and segmentate the tumor spheroids in real-time for the size and shape calculation. A simple off-chip solenoid valve-based sorting actuation module was proposed to sort out target tumor spheroids with the desired size and shape. Utilizing the developed sorter, we successfully uncovered the drug response variations on cisplatin of lung tumor spheroids in the same population but with different sizes and shapes. Moreover, with this sorter, the precision of drug testing on the spheroid population level was improved to a level comparable to the precise but complex single spheroid analysis. The developed sorter also exhibits significant potential for organoid morphology and sorting for precision medicine research.

Phenoconversion Due to Drug-Drug Interactions in CYP2C19 Genotyped Healthy Volunteers.

To compensate for drug response variability, drug metabolism phenotypes are determined based on the results of genetic testing, and if necessary, drug dosages are adjusted. In some cases, discrepancies between predicted and observed phenotypes (phenoconversion) may occur due to drug-drug interactions caused by concomitant medications. We conducted a prospective, exploratory study to evaluate the risk of CYP2C19 phenoconversion in genotyped healthy volunteers exposed to CYP2C19 inhibitors. Three groups of volunteers were enrolled: CYP2C19 g-RM, g-NM, and g-IM (g- for genetically predicted). All volunteers received as CYP2C19 phenotyping substrate 10 mg omeprazole (OME) alone at the control session and in co-administration with CYP2C19 inhibitors: voriconazole 400 mg and fluvoxamine 50 mg in second and third study sessions, respectively. Phenoconversion occurred in over 80% of healthy volunteers, with variations among genotypic groups, revealing distinct proportions in response to fluvoxamine and voriconazole. Statistically significant differences were observed in mean metabolic ratios between CYP2C19 intermediate metabolizers (g-IMs) with *1/*2 and *2/*17 genotypes, with the *2/*17 group exhibiting lower ratios, and distinctions were noted between genotypic groups, emphasizing the impact of genetic variations on drug metabolism. When reclassified according to CYP2C19 baseline-measured phenotype into p-RM, p-NM, and p-IM (p- for measured phenotype), we observed 100% phenoconversion of p-RMs and a significant phenotype switch in p-NMs, p-IMs, and p-PMs after fluvoxamine and voriconazole, and complete phenoconversion of p-IMs to p-PMs on both inhibitors, emphasizing the impact of genetic variations on the vulnerability to CYP2C19 phenoconversion and the importance of considering both genotyping and phenotyping in predicting drug response.

Pharmacogenomic Polygenic Model of Clopidogrel Predicts Recurrent Ischemic Events in Chinese Patients With Coronary Artery Disease

Patients with coronary artery disease (CAD) need to take antiplatelet drugs regularly in order to prevent thrombosis; however, there is existing inter-individual variability in drug response. Pharmacogenomic studies indicate that drug response may also be influenced by genetic variants, and multiple genetic variants may work together. We assumed that patients carrying more risk alleles might have a worse clopidogrel drug response and that a polygenic model integrated different single variants might have the potential to explain clopidogrel drug response variability better. We aimed to investigate whether the polygenic model could be used to predict clopidogrel drug response. A total of 935 CAD patients were enrolled in the study. We investigated the association between 19 clopidogrel-related single-nucleotide polymorphisms (SNPs) and the incidence of recurrent ischemic events. Additionally, a polygenic model was constructed to assess the risk of ischemic events. There were only 2 SNPs of CYP2C8 gene (rs1934980 and rs17110453) that were nominally associated with incidence of recurrent ischemic events. We constructed a polygenic model integrated with 6 clopidogrel-related SNPs. When compared with patients carrying 6 or fewer risk alleles, patients with 7 or more risk alleles had a higher risk of ischemic events (hazard ratio = 1.87; P = 0.04). The polygenetic model may be useful for clopidogrel drug response prediction in patients with CAD.

Enhanced hepatotoxicity assessment through encapsulated HepG2 spheroids in gelatin hydrogel matrices: Bridging the gap from 2D to 3D culture

Conventional 2D drug screening often fails to accurately predict clinical outcomes. We present an innovative approach to improve hepatotoxicity assessment by encapsulating HepG2 spheroids in gelatin hydrogel matrices with different mechanical properties. Encapsulated spheroids exhibit sustained liver-specific functionality, enhanced expression of drug-metabolizing enzymes, and increased drug sensitivity compared to 2D cultures. The platform detects critical variations in drug response, with significant differences in IC50 values between 2D and spheroid cultures ranging from 1.3-fold to > 13-fold, particularly for acetaminophen. Furthermore, drug-metabolizing enzyme expression varies across hydrogel concentrations, suggesting a role for matrix mechanical properties in modulating hepatocyte function. This novel spheroid-hydrogel platform offers a transformative approach to hepatotoxicity assessment, providing increased sensitivity, improved prediction, and a more physiologically relevant environment. The use of such advanced in vitro models can accelerate drug development, reduce animal testing, and contribute to improved patient safety and clinical outcomes.

APF2: an improved ensemble method for pharmacogenomic variant effect prediction

Lack of efficacy or adverse drug response are common phenomena in pharmacological therapy causing considerable morbidity and mortality. It is estimated that 20–30% of this variability in drug response stems from variations in genes encoding drug targets or factors involved in drug disposition. Leveraging such pharmacogenomic information for the preemptive identification of patients who would benefit from dose adjustments or alternative medications thus constitutes an important frontier of precision medicine. Computational methods can be used to predict the functional effects of variant of unknown significance. However, their performance on pharmacogenomic variant data has been lackluster. To overcome this limitation, we previously developed an ensemble classifier, termed APF, specifically designed for pharmacogenomic variant prediction. Here, we aimed to further improve predictions by leveraging recent key advances in the prediction of protein folding based on deep neural networks. Benchmarking of 28 variant effect predictors on 530 pharmacogenetic missense variants revealed that structural predictions using AlphaMissense were most specific, whereas APF exhibited the most balanced performance. We then developed a new tool, APF2, by optimizing algorithm parametrization of the top performing algorithms for pharmacogenomic variations and aggregating their predictions into a unified ensemble score. Importantly, APF2 provides quantitative variant effect estimates that correlate well with experimental results (R2 = 0.91, p = 0.003) and predicts the functional impact of pharmacogenomic variants with higher accuracy than previous methods, particularly for clinically relevant variations with actionable pharmacogenomic guidelines. We furthermore demonstrate better performance (92% accuracy) on an independent test set of 146 variants across 61 pharmacogenes not used for model training or validation. Application of APF2 to population-scale sequencing data from over 800,000 individuals revealed drastic ethnogeographic differences with important implications for pharmacotherapy. We thus think that APF2 holds the potential to improve the translation of genetic information into pharmacogenetic recommendations, thereby facilitating the use of Next-Generation Sequencing data for stratified medicine.

A new evaluation system for drug-microbiota interactions.

The drug response phenotype is determined by a combination of genetic and environmental factors. The high clinical conversion failure rate of gene-targeted drugs might be attributed to the lack of emphasis on environmental factors and the inherent individual variability in drug response (IVDR). Current evidence suggests that environmental variables, rather than the disease itself, are the primary determinants of both gut microbiota composition and drug metabolism. Additionally, individual differences in gut microbiota create a unique metabolic environment that influences the in vivo processes underlying drug absorption, distribution, metabolism, and excretion (ADME). Here, we discuss how gut microbiota, shaped by both genetic and environmental factors, affects the host's ADME microenvironment within a new evaluation system for drug-microbiota interactions. Furthermore, we propose a new top-down research approach to investigate the intricate nature of drug-microbiota interactions in vivo. This approach utilizes germ-free animal models, providing foundation for the development of a new evaluation system for drug-microbiota interactions.

Effect on emergence from anesthesia following induction with diazepam and its association with CYP2C9, CYP2C19 and CYP3A4 gene polymorphisms

Introduction &amp; Objectives: Benzodiazepines are commonly used as adjuvants to IV anesthetic agents. Diazepam dosage varies widely depending on the desired endpoints. The rationale behind such inter-individual variability in drug responses has been attributed to genetically determined alterations in the enzyme activities. We assessed the emergence time in patients after induction of anesthesia with two different doses of diazepam and evaluated if any correlation exists between the emergence time and genetic polymorphism in drug metabolizers CYP2C19, CYP2C9, and CYP3A4 genes. Methodology: The study was conducted on randomly selected adult patients scheduled for elective surgery between 40-60 y of age with predefined inclusion and exclusion criteria. The patients were distributed into 2 groups, with group 1 receiving 0.2 mg/kg and group 2 given 0.3 mg/kg diazepam. Wake-up time after surgery was determined after a standardized verbal command played by a tape-recorder. Three ml blood in Ethylenediaminetetraacetic acid (EDTA) was collected before administering anesthesia. Results: Wake-up time between 8-18 min did not vary between the two groups. Comparatively persons having 2C19 2 took longer time to wake-up but this association was not statistically significant. However, CYP2C9 2 and 2 C9 3 and CYP3A4 genotypes had no influence on wake-up time. Conclusion: The pilot study suggests the emergence time from diazepam is longer in patients having 2C19 2 which confers slow-metabolizer phenotype. However, this study needs to be replicated in larger sample size to arrive at definitive conclusions before clinical applications. Abbreviations: BDZ- benzodiazepine; EDTA- Ethylenediaminetetraacetic acid; GA-General Anesthesia Key words: Diazepam; Emergence; Gene Polymorphism; Pharmacogenetics Citation: Mittal T, Badhe VK, Badhe DSD, Ahluwalia P, Mitta Bl, Mittal R. Effect on emergence from anesthesia following induction with diazepam and its association with CYP2C9, CYP2C19 and CYP3A4 gene polymorphisms. Anaesth. pain intensive care 2024;28(1):126-138. DOI: 10.35975/apic.v28i1.2380 Received: July 20, 2023; Reviewed: December 07, 2023; Accepted: December 07, 2023

A study of cutaneous adverse drug reactions in the dermatology department of a tertiary care teaching hospital in Gujarat

Background: Various drugs are responsible for different cutaneous adverse drug reactions (CADRs). Considering variation in drug responses and the day-to-day increasing burden of ADRs, this study was done with emphasis on the need for effective evaluation and the reporting of the ADR reporting programme. Methods: This was an observational cross-sectional study conducted for the duration of six months in the dermatology department to evaluate various clinical patterns of CADRs. Results: A total of 60 CADRs were reported. Among them, 51.67% were present in males and 48.33% were present in females. The most common CADR was FDE (35%), followed by macula-papular rash (25%). Antimicrobials were most commonly responsible for CADRs, followed by NSAIDs, antiepileptic, anti-gout, and anti-hypertensive medications. Conclusions: For better patient care, drug safety, and rational use of medicines, knowledge of various drugs responsible for CADRs can be useful for health care professionals to reduce mortality and morbidity by monitoring, reporting, and assessment of CADRs whenever detected.

Pharmacogenomics as a Tool in Addressing Genetic VariationDependent Adverse Drug Reactions

Adverse drug reactions (ADRs) are a cause of discontinuing drug development and withdrawal from market, as well as a very common source of morbidity and mortality. Genetic variables may be the primary predictor of drug response for certain medications, but they are estimated to account for 15– 30% of the variability in drug response. Many factors can contribute to adverse drug reactions, including genetics and drug targeting/delivery. Genetic markers, such as single nucleotide polymorphisms (SNPs) in drug-metabolizing enzymes, drug targets, and human leukocyte antigen (HLA) genotypes, have been associated with an increased risk of ADRs. Pharmacogenomics is the study of the genetic variation in the way that different people react to different pharmaceuticals, including variations in the risk of adverse drug reactions, dose requirements, and efficacy. The implementation of genetic data for predicting responses to medications and ADRs is becoming a reality in clinical practice, offering the potential to reduce the incidence of ADRs and improve patient outcomes. As pharmacogenomic research continues to advance, it holds great promise for enhancing drug safety and efficacy, ultimately leading to more tailored and effective therapeutic interventions.