Drug Response Research Articles

Introduction: During postnatal development, the heart undergoes continuous adaptation to mechanical loads—primarily preload (stretch experienced during chamber filling) and afterload (resistance encountered during blood ejection). Engineered cardiac tissues (ECTs) offer a promising system to investigate cardiac development and diseases in vitro . Here, we present the “crossbow” bioreactor, designed to independently modulate preload and afterload in ECTs. This system enables the application of time-varying mechanical loads, facilitating the study of their individual and combined effects on cardiomyocyte phenotype and function. Methods: Cylindrically shaped ECTs (“cardiobundles”) are fabricated with neonatal rat ventricular myocytes suspended in a fibrin-matrigel hydrogel. The cardiobundles are mounted onto the crossbow bioreactor, attached on one side to a curved polydimethylsiloxane cantilever arm and on the other side to a ratcheted center beam using a stainless-steel ring. Silk sutures that tether the cantilever arm to the beam are traversed down the beam to deflect the cantilever arm and increase afterload. The position of the stainless-steel ring is also changed to stretch the cardiobundles, increasing preload. In addition to a group with constant mechanical loading, preload, afterload, and combined conditions were assessed using force testing, optical mapping, immunostaining, and bulk RNA-sequencing. Results: Cardiobundles experiencing increased afterload exhibited the highest contractile forces. Control and loaded tissues exhibited uniform action potential propagation and similar conduction velocities and action potential durations. Increased preload led to increased total muscle mass and greater cardiomyocyte cell cycling. Gene set enrichment analysis revealed significant upregulation of genes related to cell-cycling and downregulation of genes related to oxidative metabolism, action potentials, and cell-cell junctions in mechanically loaded groups compared to controls. Conclusions: Overall, this work introduces a novel bioreactor system to elucidate the effects of time-varying mechanical loading on ECTs. While increased preload stimulated cardiomyocyte proliferation, increased afterload led to enhanced contractile force generation. The combined effects of preload and afterload yielded distinct cellular and functional adaptations, providing valuable insights for future use of this platform in studies of ECT maturation and drug response.

Background: Betablockers including Metoprolol are commonly used to treat hypertension but have adverse drug responses like bradycardia and hypotension. Genetic polymorphism in cytochrome P450 2D6 is responsible for interpatient differences in drug response because this enzyme plays an important role in metabolizing metoprolol. There are pharmacogenomic guidelines available for treatment of heart failure, but not much information exists regarding patients with hypertension. Therefore, this study aims to investigates CYP2D6 genotype–metoprolol–ADR relationships in hypertensive patients to inform precision pharmacovigilance and develop AI pharmacovigilance system. Methods: We retrospectively analyzed Individual Case Safety Reports (ICSRs) from the FDA Adverse Event Reporting System (FAERS) from January 1997 to March 2025. We used filter such as reports with hypertension as the primary indication for metoprolol. Gene–drug–ADR associations were extracted from PharmGKB, focusing on CYP2D6 alleles with Level 1A&Level 3 evidence. We categorised ADRs using MedDRA terms; serious events involved hospitalization, life-threatening conditions, or death. The primary exposure and outcome were CYP2D6 genotype and metoprolol-associated ADRs respectively. We obtained Allele frequencies from gnomAD for European (EUR), East Asian (EAS), and African (AFR) populations. Descriptive statistics was used to summarize ADR frequencies and demographics whereas Chi-square tests compared ADR rates by sex and ancestry Odds ratios (OR), 95% confidence intervals (CIs) and p-values quantified associations. Data processing&analysis was performed using Python. Public de-identified data exempted from ethical approval. Results: A total of 10,407 ICSRs were included; 53.6% female, 38.6% male. Over 300 ADRs were reported including drug ineffective (3.4%)&bradycardia (3%). Serious ADRs occurred in 22% of cases. CYP2D6*4 (no function) allele frequency was ~20% in EUR but rare in EAS, where *1 and *2 alleles predominate. EUR showed higher odds of bradycardia (OR 1.42, 95% CI 1.12–1.80, p=0.002) compared to other ancestries. No significant sex differences in ADR rates were observed (p=0.19). Conclusion: CYP2D6 genetic polymorphism is related to variable risk of ADRs with metoprolol in patients with hypertension, with population-dependent allele frequencies determining risk profiles. These data lend credence to the possible utility of genotype-stratified beta-blocker treatment in hypertension.

Background: Pharmacogenomics is increasingly recognized as a key factor in optimizing antihypertensive therapy. Genetic polymorphisms may influence individual responses to commonly prescribed antihypertensives, particularly angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs). This systematic review and meta-analysis evaluates the impact of genetic variants on the antihypertensive efficacy of ACEIs versus ARBs in adults. Methods: A systematic search was conducted across published meta-analyses and systematic reviews written in English, focusing on adult populations (≥18 years) treated with ACEIs or ARBs. Studies were included if they examined the influence of genetic polymorphisms—such as those in the ADRB2, AGT, ACE, or CYP11B2 genes—on blood pressure response. Eligible studies compared ACEIs and ARBs directly or within the context of genetic stratification. Data were extracted on gene-drug interactions, blood pressure outcomes, and methodological quality. Results: A forest plot was generated to visually summarize genotype-specific differences in blood pressure response to ACE inhibitors versus ARBs. Several polymorphisms were consistently associated with differential responses to RAAS-targeting therapies. ACEI efficacy was particularly influenced by the I/D polymorphism in the ACE gene, while M235T variants in the AGT gene and -344T/C in CYP11B2 impacted ARB response. Polymorphisms in ADRB2, such as rs1042713, were also linked to differential outcomes. ARBs demonstrated more consistent blood pressure reductions across genetic backgrounds, while ACEIs showed greater variability. Studies suggest that pharmacogenetic-guided therapy may improve treatment precision and clinical outcomes. Conclusions: Genetic polymorphisms significantly influence antihypertensive responses, with ACEIs showing more genotype-dependent effects. ARBs may offer a more predictable therapeutic profile in genetically diverse populations. These findings support the integration of pharmacogenomic testing into hypertension management to enable individualized therapy.

Background: Resistant hypertension affects approximately 10-20% of hypertensive patients. Emerging evidence suggests genetic variation significantly influences antihypertensive drug response, providing a compelling rationale for pharmacogenomic-guided therapy. Research Question: Does pharmacogenomic testing lead to clinically meaningful improvements in blood pressure control, medication management, and adverse effect profiles in resistant hypertension? Methods: This systematic review synthesizes evidence on pharmacogenomic markers associated with antihypertensive drug response in resistant hypertension. We conducted a comprehensive search of PubMed and PharmGKB for studies published between Jan 2015 to Mar 2025. Two independent reviewers screened 412 articles, with 87 meeting inclusion criteria. Meta-analysis was performed for genetic variants reported in ≥3 studies with comparable outcome measures. Results: CYP2D6 variants (*4, *10) significantly affect metoprolol metabolism (p<0.001), with poor metabolizers experiencing a 2.3-fold increased risk of bradycardia and hypotension (95% CI: 1.7-3.1). ABCB1 C3435T polymorphism impacts verapamil efficacy, with T allele carriers showing a mean 8.4 mmHg greater systolic BP reduction compared to wild-type carriers (p=0.003). NPPA T2238C and NPPB rs198358 variants predict response to aldosterone antagonists with an area under the curve of 0.76 (95% CI: 0.68-0.84). CYP11B2 -344C>T variants demonstrated strong associations with spironolactone efficacy (odds ratio: 2.1; 95% CI: 1.4-3.2). A panel of 12 SNPs in genes including YEATS4, SLC12A3, and WNK1 demonstrated 78% predictive accuracy for thiazide diuretic response in black patients with resistant hypertension. Meta-analysis of implementation studies (n=891 patients across 5 studies) indicates a 17.3% improvement in blood pressure control (95% CI: 12.8-21.9%, p<0.001) compared to standard care, with a 32% reduction in medication changes (p=0.002) and 26% decrease in reported adverse effects (p=0.005). Conclusions: Pharmacogenomics offers a promising strategy to overcome treatment resistance in hypertension by enabling personalized medication selection based on individual genetic profiles. Implementation of pharmacogenomic testing could improve blood pressure control rates, reduce adverse effects, and decrease polypharmacy burden in resistant hypertension. Future prospective studies are needed to validate comprehensive multi-gene panels specific to resistant hypertension.

Single-cell sequencing has revolutionized our understanding of cellular heterogeneity and responses to environmental stimuli. However, mapping transcriptomic changes across diverse cell types in response to various stimuli and elucidating underlying disease mechanisms remains challenging. Here we present Squidiff, a diffusion model-based generative framework that predicts transcriptomic changes across diverse cell types in response to environmental changes. We demonstrate the robustness of Squidiff across cell differentiation, gene perturbation and drug response prediction. Through continuous denoising and semantic feature integration, Squidiff learns transient cell states and predicts high-resolution transcriptomic landscapes over time and conditions. Furthermore, we applied Squidiff to model blood vessel organoid development and cellular responses to neutron irradiation and growth factors. Our results demonstrate that Squidiff enables in silico screening of molecular landscapes and cellular state transitions, facilitating rapid hypothesis generation and providing valuable insights into the regulatory principles of cell fate decisions.

Single-tube Lambda exonuclease-mediated LbuCas13a detect of ssDNA for single-nucleotide polymorphisms genotyping.

Development of a microelectrode array system for simultaneous measurement of field potential and glutamate release in brain slices.

Insights into the role of gut microbiota modulation in the management of various cardiovascular diseases: A new approach for improving the efficacy of current cardiovascular medications.

ABSTRACTBackgroundSingle nucleotide polymorphisms (SNPs) located in the genes participating in the steroid hormone metabolism pathway may influence the outcomes of androgen deprivation therapy (ADT) in prostate cancer (PCa) patients, but findings on the Chinese population remain limited.MethodsA multicentric cohort of 301 Chinese PCa patients receiving first‐line ADT was enrolled. Germline SNPs located in 62 steroid hormone metabolism‐related genes were analyzed for associations with time to ADT failure using multivariate Cox regression. Important expression quantitative trait loci (eQTLs) were discovered.ResultsFour SNPs were significantly associated with time to ADT failure: rs36119043 in AKR1D1 (hazard ratio, HR = 2.02, 95% confidence interval, 95% CI: 1.44–2.85, p = 5.72 × 10−5), rs151155810 in HSD17B12 (HR = 7.87, 95% CI: 2.78–22.30, p = 1.05 × 10−4), rs71179009 in SULT2B1 (HR = 2.16, 95% CI: 1.44–3.22, p = 1.85 × 10−4), rs28609134 in SRD5A3 (HR = 2.50, 95% CI: 1.51–4.15, p = 3.79 × 10−4). Potential causal eQTLs in the LD regions of these SNPs were identified, with significant impacts on AKR1D1, SULT2B1, and SRD5A3 expression in diverse tissues. A cumulative risk allele effect was observed: HR = 2.74 (95% CI: 1.86–4.03) under the dominant model and HR = 2.04 (95% CI: 1.63–2.55) under the additive model, with a median survival of 176 months (95% CI: N/A) in noncarrier patients vs. 92 months (95% CI: 65–N/A) in one risk locus‐carriers and 55 months (95% CI: 26–N/A) in two risk loci‐carriers.ConclusionsSNPs in the steroid hormone metabolism pathway can predict time to ADT failure in Chinese PCa patients, supporting their potential role for drug response and pharmacogenomic stratification.

Factors of bias in spheroid-based drug screening: Fabrication method, spheroid size, and cell viability.

Genomic variations among individuals can greatly affect their responses to different medications. Pharmacogenomics is the area of study that aims to understand the relationship between these various genetic variations and subsequent drug responses. Many medications used to optimize cardiovascular health are affected by these genetic variants and these relationships can subsequently impact dosing strategies in patients. This study aims to review the current literature on the clinical applications of pharmacogenomics for commonly used cardiovascular medications such as Warfarin, Clopidogrel, Statins, Beta Blockers, and ACE-I/ARBs. Databases like PubMed were accessed to gather background information on pharmacogenomics and to collect data on relationships between genetic variants and subsequent drug response. Information on clinical applications and guidelines was obtained by accessing the CPIC and DPWG databases. This article describes the most up-to-date data on pharmacogenomics relating to commonly used cardiovascular medications. It also discusses the clinical application of pharmacogenomic data as it pertains to medication selection/dosing by detailing current guidelines published by organizations such as the Clinical Pharmacogenetics Implementation Consortium and the Dutch Pharmacogenetics Working Group. In conclusion, this paper will help medical providers not only better understand pharmacogenomics but also apply it in their day-to-day practice. Clinical guidelines relating to the application of pharmacogenomic data were discussed both in text and graphical format, allowing providers to confidently select medications and adjust doses for common cardiovascular medications so that patients receive the maximum therapeutic benefit with minimal toxicity.

HCC remains one of the most lethal cancers globally, and accurately replicating the early events of tumor evolution remains a critical challenge. In this study, we developed early-stage liver cancer cell lines by introducing distinct combinations of oncogenes into primary mouse hepatocytes. Using 3D bioprinting technology, combined with bioinks composed of gelatin and alginate, we constructed a more precise representation of liver cancer tissue to better simulate key tumor characteristics. Our·findings revealed that different oncogene combinations produced unique drug response profiles, with Ras-driven cells exhibiting heightened sensitivity to ferroptosis. Furthermore, 3D bioprinting tumor tissues derived from transformed hepatocytes effectively captured early HCC characteristics. These models preserved key features of early-stage liver cancer and provided a reliable platform for drug screening. Importantly, the 3D models demonstrated higher resistance to chemotherapy and targeted therapies compared with 2D cultures. In summary, we established both 2D and 3D models that replicate early HCC progression, offering valuable tools for drug screening and advancing our understanding of early carcinogenic mechanisms.

Understanding drug responses at the single-cell level is crucial for identifying biomarkers and uncovering resistance mechanisms. However, existing models predominantly rely on genomic profiles, while overlooking drug structure-function relationships and showing limited generalization to novel drugs with distinct structures. To address this limitation, we propose a novel framework that integrates drug structural information with genomic data. Specifically, we develop a graph-aware Transformer to capture interatomic relations and generate joint representations linking atomic features to genomic profiles. To overcome the scarcity of single-cell drug response data, we propose a novel predictive framework that leverages prior knowledge from bulk RNA datasets through meta-pretraining and few-shot transfer learning. Furthermore, we introduce a position-based feature extraction network and a gene gradient attribution algorithm to identify key resistance genes and drug action pathways. Pre-trained on 223 drugs across 14 tissues and tested on seven single-cell datasets, our model achieves an approximate 5% improvement in accuracy for known drugs and about 20% increase in generalization to unseen drugs. This approach provides an effective method for studying drug resistance mechanisms at single-cell level, particularly for novel compounds.

Cancer therapy is rapidly evolving from a one-size-fits-all paradigm toward highly personalized approaches. Traditional chemotherapies and radiotherapies, while broadly applied, often yield suboptimal outcomes due to tumor heterogeneity and are limited by significant toxicities. In contrast, precision oncology tailors prevention, diagnosis, and treatment to the individual patient’s genetic and molecular profile. Key advancements underscore this shift: molecularly targeted drugs (e.g., trastuzumab for HER2-positive breast cancer, EGFR and ALK inhibitors for lung cancer) have improved efficacy and reduced toxicity compared to conventional therapy. Pharmacokinetic (PK) and pharmacodynamic (PD) considerations are central to personalizing treatment, explaining variability in drug exposure and response among patients and guiding dose optimization. Modern strategies like therapeutic drug monitoring and model-informed precision dosing seek to maintain drug levels in the therapeutic range, improving outcomes. Immunotherapies, including checkpoint inhibitors and CAR-T cells, have transformed oncology, though patient selection via biomarkers (such as PD-L1 expression or tumor mutational burden) is critical to identify likely responders. Innovative drug delivery systems, notably nanomedicine, address PK challenges by enhancing tumor-specific drug accumulation and enabling novel therapeutics. Furthermore, rational combination regimens (informed by PK/PD and tumor biology) are being designed to achieve synergistic efficacy and overcome resistance. Key barriers include the high cost of biomarker testing, insufficient laboratory infrastructure, and inconsistent reimbursement policies. Operational inefficiencies such as long turnaround times or lack of clinician awareness further limit the use of precision diagnostics. Regulatory processes also remain complex, particularly around the co-development of targeted drugs and companion diagnostics, and the evidentiary requirements for rare subgroups. Addressing these barriers will require harmonized policies, investment in infrastructure, and educational initiatives to ensure that the promise of personalized medicine becomes accessible to all patients. Ensuring that advances are implemented responsibly—guided by pharmacological insights, supported by real-world evidence, and evaluated within ethical and economic frameworks—will be critical to realizing the full potential of personalized cancer medicine.

Pattern recognition and machine learning algorithms have become integral to modern drug discovery, offering powerful tools to uncover complex patterns in biomedical data. This article provides a comprehensive review of state-of-the-art pattern recognition techniques—including traditional machine learning (e.g., support vector machines), deep learning approaches, genome-wide association studies (GWAS), and biomarker discovery methods—as applied in pharmacogenomics and computational drug repurposing. We discuss how these methods facilitate the identification of genetic factors that influence drug response, as well as the in silico screening of existing drugs for new therapeutic uses. Two antiviral agents, ribavirin and lopinavir, are examined as extended case studies in the context of COVID-19, illustrating practical applications of pattern recognition algorithms in analyzing pharmacogenomic data and guiding drug repurposing efforts during a pandemic. We highlight successful approaches such as the machine learning-driven prediction of responders and the AI-assisted identification of repurposed drugs (exemplified by the case of baricitinib for COVID-19), alongside current limitations, including data scarcity, model interpretability, and translational gaps. Finally, we outline future directions for integrating multi-omics data, improving algorithmic interpretability, and enhancing the synergy between computational predictions and experimental validation. The insights presented highlight the promising role of pattern recognition algorithms in advancing precision medicine and accelerating drug discovery, while recognizing the challenges that must be addressed to fully realize their potential.

Plaque psoriasis: It is the commonest type of psoriasis, a chronic, immune mediated and genetic based dermatoses, characterizing by redness, thickness and scaly plaques occurring most commonly on the elbows, knees, scalp, and lower back, but any skin surface can be involved, has a major impact on quality of life. D3 or cholecalciferol: A fat soluble vitamin that has many important and vital roles for normal body physiological activities. Certain types of food in addition to dietary supplements and exposure to sun UVB radiation are sources for this vitamin. Clobetasol: It is the perfect of topical corticosteroids, works as anti-inflammatory, anti-pruritic and vasoconstrictive properties, and reduces the activity of immune system and is currently used to treat hyperproliferation or inflammatory for plaque psoriasis. Salicylic acid: It is a keratolytic agent, acted as a scale pulley, helpful to smoothing and eradicate psoriasis scales. Complete Blood Count (CBC and Erythrocytes Sedimentation Rate (ESR). Psoriasis Area and Severity Index (PASI): is a widely used appliance in psoriasis tribunals that judges and grades the sternness of psoriatic lesions and the patient’s reaction to treatment. Aim: To explore the impact of adding urea to salicylic acid and clobetasol combination in improving their antipsoriatic activity in patients with mild to moderate plaque psoriasis, to control signs and symptoms of this disease and to improve the quality of life. Materials and Method: This study includes 100 samples divided two groups of plaque psoriasis patients whose severity of psoriasis ranges from mild to moderate. A formula containing clobetasol and salicylic acid is given to the first group of plaque psoriasis patients (Control), and a formula containing clobetasol and salicylic acid in addition to Vitamin D3 is given to the second group of patient’s plaque psoriasis (case). The levels of lymphocytes, erythrocytes sedimentation rate (ESR) was measured, and the Score was calculated for both groups and compared with the time and drug response rate for both groups. Results: The study included 100 psoriatic patients divided to 63 were female and 37 were male. The ages of the patients ranged from 20-65 years. The WBC, lymphocytes, ESR and PASI mark were resolute before the treatment for control and case groups to be non-significant (P> 0.03, 0.09, 0.52 and 0.32). WBC and Lymphocytes, ESR and PASI score values were lower after adding vitamin D3 to the regimen of salicylic acid and clobetasol than without adding D3 to be significant (P> 0.00, 0.01, 0.02 and 0.00). Conclusion: It was found that concomitant management with vitamin D3 enhances the effectiveness of topical treatment (salicylic acid & clobetasol) as anti-psoriatic effect and reduces the time required for treatment. The signs and indicators of plaque psoriasis were controlled and the quality of life improved

Baicalin-loaded micelles: Modulating M1 macrophages to overcome Lenvatinib resistance in anaplastic thyroid carcinoma.

T-cell leukemias and lymphomas (TCLs) form a heterogeneous group of rare and often aggressive malignancies. Due to the rarity and heterogeneity of TCL subtypes, clinical trials are challenging to conduct, making pharmacogenomic studies in cell line panels critical for the discovery of targeted therapeutics. The scarcity of data repositories with integrated multi-omics and drug screening data hinders the preclinical evaluation of drug vulnerabilities and the identification of molecular markers predictive of responses to monotherapies and combinations. To address this gap, we conducted comprehensive pharmacogenomic profiling on a panel of 38 TCL cell lines, representing major clinical TCL subtypes to capture the molecular and phenotypic diversity. The TCL-38 multi-omics data resource includes harmonized genetic, molecular, and epigenetic profiles, with comprehensive annotations, and standardized drug response assessment of each cell line. This resource, together with machine learning predictions, was leveraged to identify TCL subtype-specific therapeutic vulnerabilities, including single-agent sensitivities and synergistic drug combinations, which were linked to genetic or epigenetic features as potential predictive biomarkers. This integrated and openly available resource (https://aittokallio.group/tcl38) could help advance the currently limited treatment options for patients with TCL.

m6A-centered crosstalk with epigenetic regulation(m6A-CT) is essential for understanding disease development and drug response. Based on different layers of epigenetic regulation, m6A-CT can be classified into four categories: m6A-centered crosstalk with histone modification (m6A-HistMod), m6A-centered crosstalk with DNA methylation (m6A-DNAMeth), m6A-centered crosstalk with RNA modification (m6A-RNAMod), and m6A-centered crosstalk with non-coding RNA (m6A-ncRNA). However, none of the existing databases hascomprehensively provided the crucial data regarding m6A-CT. Therefore, a significant update was made to the M6AREG database. This updated version includes 713 entries form6A-HistMod, 300 entries form6A-DNAMeth, 483 entries form6A-RNAMod, and 939 entries form6A-ncRNA. Thesetypes ofcrosstalkcan alter cellular pathways and processes, ultimately leading to the development of 271 categories of diseases and the response data of 205 drugs, which are regulated by 585 epigenetic regulators (including 138 regulatory proteins and 447 non-coding RNAs). Given that these data are critical for identifying diagnostic biomarkers and therapeutic targets, discovering drugs that target m6A modification, and developing combinatorial therapies to overcome drug resistance or immune evasion, this update will greatly enhance the impact of M6AREG and hold significant importance for m6A-relevant studies. The database is currently accessible to all users at: https://idrblab.org/m6areg/.

Drug resistance continues to be a major challenge in cancer treatment. Understanding cellular and molecular dynamics after treatment is crucial for elucidating resistance mechanisms. Single-cell RNA sequencing (scRNA-seq) of paired pre- and post-treatment patient samples enables high-resolution exploration of such dynamics, but rapidly accumulated relevant data bring challenges for easy data access, integration, and reuse. Therefore, we present the Cancer Treatment-related Single-Cell transcriptome DataBase (scCT-DB, http://scctdb.ncpsb.org.cn). scCT-DB has comprehensively collected 266 patient-derived paired pre- and post-treatment scRNA-seq datasets processed by a uniform pipeline and with detailed and structured metadata, most of which simultaneously include information on primary/acquired drug response. scCT-DB includes 6.19 million cells from 1142 original patient samples, 27 major cancer types, 96 therapeutic regimens, and 102 drugs (involving chemotherapy, immunotherapy, hormone therapy, and targeted therapy). scCT-DB also provides 48 dataset pairs with opposite primary response groupings and 41 longitudinal datasets with ≥3 sampling timepoints. Besides data browsing, download, and search, scCT-DB also supports single-dataset analysis (including cell abundance, gene, cell state, and intercellular communication perturbation analyses), dataset comparative and re-combination analyses, providing insights into drug perturbation mechanisms and their heterogeneity in patients, drug resistance mechanisms, and discovery of biomarkers predictive of treatment response, etc.