Variability In Drug Response Research Articles

Proximal tubule-on-chip as a model for predicting cation transport and drug transporter dynamics

Deciphering the sources of variability in drug responses requires to understand the processes modulating drug pharmacokinetics. However, pharmacological research suffers from poor reproducibility across clinical, animal, and experimental models. Predictivity can be improved by using Organs-on-Chips, which are more physiological, human-oriented, micro-engineered devices that include microfluidics. OoC are particularly relevant at the fundamental and preclinical stages of drug development by providing more accurate assessment of key pharmacokinetic events. We have developed a proximal tubule-on-a-chip model combining commercial microfluidic and chip technologies. Using the RPTEC/TERT1 cell line, we set up a dual-flow system with antiparallel flows to mimic the dynamics of blood and urine. We assessed transporters mRNA expression, cellular polarization and protein expression via immunofluorescence, and monitored the transcellular transport of prototypic xenobiotics by determining their efflux ratios. Our results show that flow exposure significantly modulate mRNA expression of drug membrane transporters. Dynamic conditions also enhance cell polarization, as evidenced by preferential basal and apical expressions of Na + /K + -ATPase, P-gp, OCT2, and MATE1 , as well as the cellular secretory profile. We demonstrated unidirectional transcellular transport of metformin with a higher efflux than influx ratio, inhibited with OCT2 inhibitor, thus confirming the relevance of our proximal tubule-on-a-chip set up for cation transport investigations. Our proximal tubule-on-a-chip can also be used to explore the interactions between transporters, xenobiotics, and endogenous metabolites, possibly involved in the variability of individual drug responses. This study provides additional evidence that OoC can help bridge the gaps between systemic and local pharmacokinetics.

Pharmacogenomic landscape of the Thai population from genome sequencing of 949 individuals

Inter-individual variability in drug responses is significantly influenced by genetic factors, underscoring the importance of population-specific pharmacogenomic studies to optimize clinical outcomes. In this study, we analyzed whole genome sequencing data from 949 unrelated Thai individuals and conducted an in-depth analysis of 3239 genes involved in drug pharmacokinetics, pharmacodynamics, or immune-mediated adverse drug reactions. We identified 43 single nucleotide polymorphisms (SNPs), 134 diplotypes, and 15 human leukocyte antigen (HLA) alleles, all with moderate to high clinical significance. On average, each Thai individual carried 14 SNPs, one to two HLA alleles, and six diplotypes with actionable phenotypic associations. Clinically important diplotypes were present in over 20% of individuals for seven genes (CYP2A6, CYP2B6, CYP2C19, CYP3A5, NAT2, SLCO1B1, and VKORC1). In addition, clinically significant SNPs with allele frequencies exceeding 20% were identified among 15 genes, including VKORC1, CYP4F2, and ABCG2. We also identified 21,211 potentially deleterious variants among 3239 genes. Of these variants, 3746 were novel. The comprehensive dataset from this study serves as a valuable resource of pharmacogenomic variants in the Thai population, which will facilitate the development of personalized drug therapies and enhance patient care in Thailand.

Impact of CYP2C19 Phenotype on Escitalopram Response in Geriatrics: Based on Physiologically‐Based Pharmacokinetic Modeling and Clinical Observation

Escitalopram is commonly prescribed for depressive and anxiety disorders in elderly patients, who often show variable drug responses and face higher risks of side effects due to age‐related changes in organ function. The CYP2C19 polymorphism may further affect escitalopram pharmacokinetics in elderly patients, complicating dose optimization for this group. Previous pharmacogenetic studies examining the impact of CYP2C19 phenotype on escitalopram treatment outcomes have primarily focused on younger adults, leaving a gap in understanding its effects on the elderly. The aim of this investigation is to determine the impact of CYP2C19 phenotypes on escitalopram exposure in geriatrics using a physiologically‐based pharmacokinetic (PBPK) model with geriatric‐specific parameters and our clinical sample of 88 elderly patients with major depressive disorder. Based on PBPK simulations, the exposure of escitalopram in CYP2C19 poor metabolizers (PMs) was 2.1‐fold higher compared with CYP2C19 extensive metabolizers (EMs). In line with PBPK results, the dose‐normalized trough concentration in our clinical sample varied according to CYP2C19 phenotype (P = 0.0132), with PMs having a 1.6‐fold higher concentration than EMs. Based on simulated and observed results, it is suggested that an escitalopram dose of 10 mg/day maybe appropriate for PMs, while a maximum dose of 20 mg/day could be used for EMs and IMs who do not achieve therapeutic responses at 10 mg/day. These findings suggest that CYP2C19 genotyping in elderly patients could be beneficial for tailoring dosing regimens in clinical practice, potentially improving treatment outcomes and reducing the risk of adverse drug reactions associated with escitalopram in this vulnerable group.

Genetic polymorphisms influencing antihypertensive drug responses.

Hypertension is a major contributor to cardiovascular disease and its associated morbidity and mortality. The low efficacy observed with some anti-hypertensive therapies has been attributed partly to inter-individual genetic variability. This paper reviews the major findings regarding these genetic variabilities that modulate responses to anti-hypertensive therapies such as angiotensin converting enzyme (ACE) inhibitors, angiotensin receptor blockers (ARBs), diuretics, calcium channel blockers (CCBs) and β-adrenoceptor blockers. The importance of studying these genetic polymorphisms stems from the goal to optimise anti-hypertensive therapy for each individual patient, aiming for the highest efficacy and lowest risk of adverse effects. It is important to recognise that environmental and epigenetic factors can contribute to the observed variations in drug responses. Owing to the multigenic and multifactorial nature of drug responses, further research is crucial for translating these findings into clinical practice and the establishment of reliable recommendations.

Severe toxicities in amazonian populations and the role of precision medicine in acute lymphoblastic leukemia treatment.

Corticosteroids, such as prednisone or dexamethasone, constitute integral components of antineoplastic regimens for Acute Lymphoblastic Leukemia (ALL) therapy, albeit accompanied by significant adverse effects. The multifactorial nature of interindividual variability in drug response, encompassing genetic polymorphisms, underscores the complexity of pharmacotherapy outcomes. However, pharmacogenetic investigations hitherto have predominantly focused on cohorts of European and North American descent, thus limiting the generalizability of findings to populations with minimal representation. Indigenous populations in Brazil, particularly those inhabiting the Amazon region, exhibit a distinctive genetic heritage, predominantly characterized by Native American ancestry. These populations frequently manifest suboptimal therapeutic responses and elevated mortality rates following ALL treatment. Therefore, delineating the molecular signatures of genes implicated in the corticosteroid pathway within these indigenous cohorts assumes paramount importance. This study identified novel variants within genes associated with the glucocorticoid pathway in indigenous Amazonian populations and conducted comparative analyses of variant frequencies across diverse global populations. The findings underscore the genetic uniqueness of indigenous groups and highlight the potential impact of genetic factors on adverse responses to ALL treatment. Precision medicine approaches tailored to the genetic peculiarities of indigenous populations emerge as imperative strategies for optimizing therapeutic efficacy and mitigating treatment-related toxicities in these communities.

Assessment of knowledge, perceptions, and readiness of healthcare professionals towards clinical pharmacogenomics implementation in Qatar: a mixed-method study.

Pharmacogenomics implementation in clinical practice is anticipated to improve our understanding of individual variations in drug response and optimise the safety and efficacy of drug therapy. We aimed to assess the knowledge, perceptions, and readiness of physicians, pharmacists, and nurses in Qatar regarding the implementation of clinical pharmacogenomics. A mixed-method study with an explanatory sequential design was conducted. Phase I was the quantitative phase which involved sending an online survey to physicians, pharmacists, and nurses. Phase II was the qualitative phase which involved conducting focus group discussions. A total of 802 responses were collected, with a response rate of 20%. Only 15.4% of participants had previous pharmacogenomics-related training. The median knowledge score for healthcare professionals was 4 out of 10 denoting low level of knowledge. However, compared to other professions, pharmacists had a higher knowledge score (p-value <0.001) and Doctor of Pharmacy (PharmD) holders scored higher than BSc holders (p-value <0.001). Despite the low level of knowledge, perceptions of healthcare professionals were positive. In addition, the majority believed knowledge of pharmacogenomics is necessary and that counselling patients on pharmacogenomics requires specialised training pharmacogenomic principles in practice. The main themes extracted from the focus group discussions were knowledge, outcome expectations, preparedness, facilitators, barriers, public education, and implementation planning. Regarding readiness, most healthcare professionals reported that they are not currently confident in applying. Healthcare providers have a low level of knowledge of pharmacogenomics. Despite this, the majority have positive perceptions towards its implementation in practice. Compared to other professionals, pharmacists with a PharmD degree scored higher in the knowledge assessment. Most healthcare providers report low confidence regarding the readiness for the implementation of pharmacogenomics and report a lack of knowledge, specialised training, and clinical guidelines as barriers.

PGxDB: an interactive web-platform for pharmacogenomics research.

Pharmacogenomics, the study of how an individual's genetic makeup influences their response to medications, is a rapidly evolving field with significant implications for personalized medicine. As researchers and healthcare professionals face challenges in exploring the intricate relationships between genetic profiles and therapeutic outcomes, the demand for effective and user-friendly tools to access and analyze genetic data related to drug responses continues to grow. To address these challenges, we have developed PGxDB, an interactive, web-based platform specifically designed for comprehensive pharmacogenomics research. PGxDB enables the analysis across a wide range of genetic and drug response data types - informing cell-based validations and translational treatment strategies. We developed a pipeline that uniquely combines the relationship between medications indexed with Anatomical Therapeutic Chemical (ATC) codes with molecular target profiles with their genetic variability and predicted variant effects. This enables scientists from diverse backgrounds - including molecular scientists and clinicians - to link genetic variability to curated drug response variability and investigate indication or treatment associations in a single resource. With PGxDB, we aim to catalyze innovations in pharmacogenomics research, empower drug discovery, support clinical decision-making, and pave the way for more effective treatment regimens. PGxDB is a freely accessible database available at https://pgx-db.org/.

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.

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.

EFFECTS OF GENETIC &amp; ENVIRONMENTAL FACTORS ON THE PHARMACOKINETICS OF CEFUROXIME FOLLOWING INTRAMUSCULAR ADMINISTRATION IN HEALTHY ADULT MALES FROM PAKISTAN

Background:Cefuroxime is an extensively prescribed broad-spectrum beta-lactam antibiotic employed to treat a wide range of bacterial infections. Variability in drug response and pharmacokinetics due to genetic and environmental variations among population subsets during clinical trials necessitate the need for a comprehensive study of the drug’s pharmacokinetics in the Pakistani population.Materials &amp; Methods:The pharmacokinetic study was carried out on eight adult male healthy volunteers at the dose of 10.7 mg/kg/intramuscular. Samples of blood were taken at predetermined time intervals 0, 0.25, 0.5, 1, 1.5, 2, 2.5, 3, 4, 5, and 6 hours. High performance liquid chromatography (HPLC) was used to measure the cefuroxime concentration in plasma. Pharmacokinetics of cefuroxime, in the Pakistani population, were determined by plasma concentration time curve using two compartment open model without lag time.Results:Pharmacokinetic parameters (mean±SD) were calculated and came out to be; maximum plasma concentration (Cmax) 31.35±0.90μg/ml, time to reach maximum plasma concentration (Tmax) 0.75±0.02h, volume of distribution (Vd) (0.28±0.02l/kg, half-life (t1/2) 1.4±0.1h, area under curve (AUC) 78.7±3.56μg h/mL.Conclusion:In the Pakistani population it is suggested that minimum inhibitory concentration can be achieved by 1.5μg/mL of plasma level. The optimum dosage regimen of 7.31 mg/kg of body weight for the primary dose, and 7.16 mg/kg of body weight as the maintenance dose, to be repeated every 8 hours.

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.

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.

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.