Liver cytochromes (CYPs) play an important role in drug metabolism but display a large interindividual variability resulting both from genetic and environmental factors. Most drug dose adjustment guidelines are based on genetics performed in healthy volunteers. However, hospitalized patients are not only more likely to be the target of new prescriptions and drug treatment modifications than healthy volunteers, but will also be more subject to polypharmacy, drug-drug interactions, or to suffer from disease or inflammation affecting CYP activities.

We compared predicted phenotype based on genetic data and measured phenotype using the Geneva cocktail to determine the extent of drug metabolizing enzyme variability in a large population of hospitalized patients (>500) and healthy young volunteers (>300). We aimed to assess the correlation between predicted and measured phenotype in the two populations.

We found that, even in cases where the genetically predicted metabolizer group correlates well with measured CYP activity at group level, this prediction lacks accuracy for the determination of individual metabolizer capacities. Drugs can have a profound impact on CYP activity, but even after combining genetic and drug treatment information, the activity of a significant proportion of extreme metabolizers could not be explained.

Our results support the use of measured metabolic ratios in addition to genotyping for accurate determination of individual metabolic capacities to guide personalized drug prescription.

The combination of Euphorbia kansui Liou ex S.B.Ho (kansui) and Glycyrrhiza uralensis Fisch (liquorice) is contraindicated in Chinese medicine, but whether it can be used in clinical practice remains controversial. The classic formula, Gansui Banxia decoction (GBD), contains kansui and liquorice, which is effective in treating an abnormal accumulation of body fluids, such as malignant ascites (MA); however, the contraindications of kansui and liquorice have limited its clinical application.

This study aims to provide a theoretical basis for the rational application of kansui-liquorice by investigating its role and mechanism in GBD.

LC-MS/MS was used to detect the metabolic differences of - glycyrrhetinic acid, glycyrrhizic acid, glycyrrhizin, glycyrrhizin, glycyrrhizin terpinolipid A, and paeoniflorin - in the liquid of MA rats before and after taking GBD. Network pharmacology was employed to predict the potential targets and mechanisms of GBD in the treatment of MA. The experimental validation was still using MA rats as a model. Flow cytometry was used to assess the expression of immune cells in blood and ascites, and the proliferation and development of T cells in bone marrow and thymus. Elisa was used to detect the content of atrial natriuretic peptide (ANP) and brain natriuretic peptide (BNP) in blood. Western blot and qRT-PCR were used to detect the expression of NPs/NPR-A/cGMP/PKG II pathway-related gene and proteins in kidney. The MA model was established by intraperitoneal injection of walker-256 cells at a concentration of 2 × 106/mL and an injection volume of 1 mL. The model was successfully established when the abdominal cavity was obviously distend and touched with a water-shaking sound, and ascites could be seen after opening the abdominal cavity.

We confirmed that GBD containing kansui-liquorice could promote the metabolism of liquorice and reduce the precipitation of toxic substances (kansuinine A). It may also target cellular immunity to exert a drug effect. Further experimental verification found that GBD containing kansui-liquorice could promote the activation of the NPs/NPRs/cGMP/PKGⅡ pathway and exert a diuretic effect in MA rats. Besides that, it could increase the proportion of CD8CD28 T cells, reduce the proportion of immune-suppressing cells, and maintain the stability of the developmental environment of the T cells.

We believe that kansui and liquorice are important components of GBD, and their combination could promote GBD to promote the clinical remission of MA through direct (activation of the NPs/NPRs/cGMP/PKGⅡ pathway) and indirect (regulating T-cell immunity) water-expelling effects.

Clopidogrel is a widely used antiplatelet agent used to prevent adverse events for patients suffering from acute coronary syndromes and ischemic stroke. As a prodrug, clopidogrel must be converted to the active form through the enzyme cytochrome (CYP) P450 2C19 (among other enzymes). Individuals carrying a loss of function (LOF) allele (i.e., *2 and/or *3) have reduced pharmacologic efficacy. Ticagrelor is an alternative antiplatelet medication that is not a prodrug.

We reviewed the Clopidogrel in High-Risk Patients with Acute Nondisabling Cerebrovascular Events (CHANCE2) Trial demonstrating the inferiority of clopidogrel dual therapy with aspirin vs ticagrelor dual therapy to prevent adverse events among patients suffering from a mild stroke among Chinese patients who carried a CYP2C19 LOF. We also summarized the pharmacogenomic testing policies within Chinese clinical laboratories after publication of this trial, and tabulated the CYP2C19 LOF allele frequencies among ancestries, as a criteria for justifying the expense required for establishing pharmacogenomic testing services for other populations.

The CHANCE2 trial showed that stroke patients carrying a CYP2C19 LOF allele(s) had a reduction of 1.6% for recurrent stroke for those treated with ticagrelor vs clopidogrel. The LOF allele frequency was highest among Pacific Island and Western and Central Asian (e.g., Han Chinese) patients and lowest among European, Latin, and Hispanic Latino patients.

Pharmacogenomic testing for CYP2C19 variants is more economically justified for laboratories that serve a population enriched with CYP2C19 LOF alleles, than populations exhibiting a lower allele frequency. Within a clinical laboratory offering testing, restricting testing to certain populations is not ethical.

Background: Studying single-nucleotide polymorphisms (SNPs) in xenobiotic-transporting and metabolizing enzyme genes before administering the doxorubicin hydrochloride and cyclophosphamide (AC) regimen may help optimize breast cancer (BC) treatment for individual patients. Objective: Genotyping specific SNPs on genes encoding for the transport and metabolism of the AC regimen and study their association with its chemotherapeutic toxicity. Method: This prospective cohort study was conducted in two hospitals in Egypt. Before receiving AC therapy, venous blood was collected from female patients with BC for DNA extraction and the genotyping of four SNPs: rs2228100 in ALDH3A1 gene, rs12248560 in CYP2C19 gene, rs1045642 in ABCB1 gene, and rs6907567 in SLC22A16 gene. Patients were then prospectively monitored for hematological, gastrointestinal, and miscellaneous toxicities throughout the treatment cycles. Results: The ALDH3A1 gene polymorphism demonstrated a significant increase in nausea, stomachache, and peripheral neuropathy among patients carrying the GC+CC genotype, compared to those with the GG genotype (p = 0.023, 0.036, and 0.008, respectively). Conversely, patients with the GG genotype exhibited significantly higher fever grades after cycles 1, 2, and 3 of the AC regimen compared to those with the GC+CC genotype (p = 0.009, 0.017, and 0.018, respectively). Additionally, fatigue severity was significantly increased among patients with the GG genotype compared to those with the GC+CC genotype following AC administration (p = 0.008). Conclusions: The SNP variation of ALDH3A1 (rs2228100) gene significantly influenced AC regimen toxicity in female BC patients. Meanwhile, SNPs in CYP2C19 (rs12248560), ABCB1 (rs1045642), and SLC22A16 (rs6907567) genes showed a significant influence on the recurrence rate of certain toxicities.

This study presents a novel approach that integrates LLMs with real-time biomedical literature to uncover drug-gene relationships, transforming how cancer researchers identify therapeutic targets, repurpose drugs, and interpret complex molecular interactions. GeneRxGPT, our user-friendly tool, enables researchers to leverage this approach without requiring computational expertise.

Major depressive disorder (MDD) is among the leading causes of disability worldwide and treatment efficacy is variable across patients. Polymorphisms in cytochrome P450 2C19 (CYP2C19) play a role in response and side effects to medications; however, they interact with other factors. We aimed to predict treatment outcome in MDD using a machine learning model combining CYP2C19 activity and nongenetic predictors.

A total of 1410 patients with MDD were recruited in a cross-sectional study. We extracted the subgroup treated with psychotropic drugs metabolized by CYP2C19. CYP2C19 metabolic activity was determined by the combination of *1, *2, *3, and *17 alleles. We tested if treatment response, treatment-resistant depression, and side effects could be inferred from CYP2C19 activity in combination with clinical-demographic and environmental features. The model used for the analysis was based on a decision tree algorithm using five-fold cross-validation.

A total of 820 patients were treated with CYP2C19 metabolized drugs. The predictive performance of the model showed at best.70 accuracy for the classification of treatment response (average accuracy = 0.65, error = ±0.047) and an average accuracy of approximately 0.57 across all the tested outcomes. Age, BMI, and baseline depression severity were the main features influencing prediction across all the tested outcomes. CYP2C19 metabolizing status influenced both response and side effects but to a lower extent than the previously indicated features.

Predictive modeling could contribute to precision psychiatry. However, our study underlines the difficulty in selecting variables with sufficient impact on complex outcomes.

Genetic polymorphism can cause variation in tramadol (TR) pharmacokinetic characteristics and the expected clinical response. In forensic toxicology, the data about parent and metabolite concentrations (MRs; metabolic ratios) could facilitate to determine the cause of death and to assess time between drug intake and death. In this study, the aim was to investigate if UGT1A8, UGT2B7, ABCC2, and SLC22A1 genotyping can facilitate interpretation by investigating the frequency of UGT1A8, UGT2B7, ABCC2, and SLC22A1 genotypes in forensic autopsy cases positive for TR and to assess whether there is a correlation between these genetic variants and MRs. Cases positive for TR (n = 48) were genotyped by HaloPlex Target Enrichment system for UGT1A8, UGT2B7, ABCC2, and SLC22A1 sequencing, in order to identify single nucleotide polymorphisms (SNPs) and/or insertion deletion (INDELs). In addition to, the concentrations of TR and its metabolites (M1 & M2) were determined by LC-MS/MS. Cases were categorized by cause of death. The investigated SNPs/INDELs were not overrepresented in any group. We found significant correlations between several loci (12 out of 73) in UGT1A8, ABCC2, and SLC22A1 genes and MRs (M2/M1, TR/M2, and TR/M1) in post-mortem TR cases. These results indicate these polymorphisms in the 4 investigated genes might influence TR pharmacokinetics leading to an unsatisfactory therapeutic effect or increasing the risk of toxicity. However, these findings should be supported in future studies with larger groups of cases.

The existing screening methods for therapeutic targets of active ingredients in traditional Chinese medicine (TCM) have problems of long detection time and high instrument cost. This article proposes a new target screening method based on virtual screening and fiber surface plasmon resonance (SPR) sensing technology, which has the characteristics of flexibility, speed, and low cost. It also reveals the target mechanism of the active ingredient isoquercitrin in the treatment of insulin resistance (IR). The binding energies of isoquercitrin with target proteins PDPK1, INSR, and PTPN1 were calculated using computer virtual methods to be -8.9, -8.9, -8.8 kcal/mol, indicating strong binding activity with isoquercitrin and predicted as three key targets. Then a fiber optic SPR biosensor functionalized with isoquercitrin molecules was constructed to detect the binding affinity between isoquercitrin and the key targets. The experimental results showed that the binding affinities of isoquercitrin to the targets PDPK1, INSR, and PTPN1 were 1.45, 1.14, and 13.21, respectively, indicating that PTPN1 is the main target of isoquercitrin in the treatment of IR. The proposed sensor has a sensitivity of 0.699 nm/(μg/ml), LOD of 0.515μg/ml, and the experimental detection time of this method is as low as 45 minutes, without the need for large and expensive optical demodulation equipment, and the device volume is 5.50 dm3, providing new ideas for the screening of therapeutic targets of active ingredients in TCM.

Background/Objectives: The accurate prediction of adverse drug reactions (ADRs) to oncological treatments still poses a clinical challenge. Chemotherapy is usually selected based on clinical trials that do not consider patient variability in ADR risk. Consequently, many patients undergo multiple treatments to find the appropriate medication or dosage, enhancing ADR risks and increasing the chance of discontinuing therapy. We first aimed to develop a pharmacogenetic model for predicting chemotherapy-induced ADRs in cancer patients (the ANTIBLASTIC DRUG MULTIPANEL PLATFORM) and then to assess its feasibility and validate this model in patients with non-small-cell lung cancer (NSCLC) undergoing oncological treatments. Methods: Seventy NSCLC patients of all stages that needed oncological treatment at our facility were enrolled, reflecting the typical population served by our institution, based on geographic and demographic characteristics. Treatments followed existing guidelines, and patients were continuously monitored for adverse reactions. We developed and used a multipanel platform based on 326 SNPs that we identified as strongly associated with response to cancer treatments. Subsequently, a network-based algorithm to link these SNPs to molecular and biological functions, as well as efficacy and adverse reactions to oncological treatments, was used. Results: Data and blood samples were collected from 70 NSCLC patients. A bioinformatic analysis of all identified SNPs highlighted five clusters of patients based on variant aggregations and the associated genes, suggesting potential susceptibility to treatment-related toxicity. We assessed the feasibility of the platform and technically validated it by comparing NSCLC patients undergoing the same course of treatment with or without ADRs against the cluster combination. An odds ratio analysis confirmed the correlation between cluster allocation and increased ADR risk, indicating specific treatment susceptibilities. Conclusions: The ANTIBLASTIC DRUG MULTIPANEL PLATFORM was easily applicable and able to predict ADRs in NSCLC patients undergoing oncological treatments. The application of this novel predictive model could significantly reduce adverse drug reactions and improve the rate of chemotherapy completion, enhancing patient outcomes and quality of life. Its potential for broader prescription management suggests significant treatment improvements in cancer patients.

Inflammation is a frequent precursor to the development of chronic pain. Ponicidin, a compound derived from traditional Chinese medicine, possesses immunomodulatory and anti-inflammatory properties. However, whether ponicidin mitigates inflammatory pain through its anti-inflammatory effects and potential target molecules remains to be further explored. In this study, we investigated the analgesic effects of ponicidin in a mouse model of Complete Freund's Adjuvant (CFA)-induced inflammatory pain and employed network pharmacology to predict the potential therapeutic targets of ponicidin for pain treatment.

Initially, we established a mouse model of inflammatory pain induced by Complete Freund's Adjuvant (CFA). Following the establishment of the model, the analgesic effects of ponicidin were assessed using behavioral tests, and further validation was conducted through hematoxylin and eosin (H&E) staining, enzyme-linked immunosorbent assay (ELISA), and immunofluorescence methods. Subsequently, we analyzed the potential analgesic targets of ponicidin using network pharmacology approaches and molecular docking.

In this study, we observed that ponicidin has a significant alleviating effect on CFA-induced inflammatory pain. Our results suggest that ponicidin may alleviate inflammatory pain by reducing inflammatory responses in the spinal cord and hind paw of CFA model mice. Furthermore, we found that ponicidin can mitigate the activation of macrophages in the subcutaneous tissue of the hind paw and microglia in the dorsal horn of the spinal cord. Network pharmacology analysis suggests that ponicidin may exert its analgesic effects through a multi-target, multi-pathway mechanism. Key transcription factors such as nuclear factor NF-κB p105 subunit (NFKB1), RELA, SP1, signal transducer and activator of transcription 3 (STAT3), and repressor element 1 silencing transcription factor (REST) may be involved in the underlying mechanisms of ponicidin's analgesic action. Through molecular docking and experimental validation, we have identified toll-like receptor 4 (TLR4) and hypoxia-inducible factor 1-alpha (HIF1A) as key targets of ponicidin's analgesic effects.

Ponicidin alleviates inflammatory pain by reducing inflammatory responses in the spinal cord and hind paw of the CFA model mice. TLR4 and HIF1A may as key targets for the analgesic effects of ponicidin.

Large language models (LLMs) have acquired a remarkable level of knowledge through their initial training. However, they lack expertise in particular domains such as genomics. Variant annotation data, an important component of genomics, is crucial for interpreting and prioritizing disease-related variants among millions of variants identified by genetic sequencing. In our project, we aimed to improve LLM performance in genomics by adding variant annotation data to LLMs by retrieval-augmented generation (RAG) and fine-tuning techniques.

Using RAG, we successfully integrated 190 million highly accurate variant annotations, curated from five major annotation datasets and tools, into GPT-4o. This integration empowers users to query specific variants and receive accurate variant annotations and interpretations supported by advanced reasoning and language understanding capabilities of LLMs. Additionally, fine-tuning GPT-4 on variant annotation data also improved model performance in some annotation fields, although the accuracy across more fields remains suboptimal. Our model significantly improved the accessibility and efficiency of the variant interpretation process by leveraging LLM capabilities. Our project also revealed that RAG outperforms fine-tuning in factual knowledge injection in terms of data volume, accuracy, and cost-effectiveness. As a pioneering study for adding genomics knowledge to LLMs, our work paves the way for developing more comprehensive and informative genomics AI systems to support clinical diagnosis and research projects, and it demonstrates the potential of LLMs in specialized domains.

We used publicly available datasets as detailed in the paper, which can be provided upon request.

Genomic medicine and pharmacogenomics (PGX) are emerging practices in medicine that play a vital role in providing personalized and efficient treatments for patients. While many countries have integrated these novel concepts into their undergraduate medical curricula to enhance the quality of healthcare, Sri Lanka remains relatively new to these advancements. Herein, we accessed the knowledge and attitude of Sri Lankan medical undergraduates on genomic medicine and PGX and explored the readiness of introducing genomic insights to Sri Lankan undergraduate medical education. The study sample was the undergraduate students of Medical Faculty of Wayamba University in Sri Lanka, being a newly developed and diverse institution seeking research findings to enhance the curriculum and teaching-learning activities aiming to produce competent graduates. A descriptive cross-sectional study was conducted by distributing a questionnaire to all five student batches at Faculty of Medicine, Wayamba University of Sri Lanka. The data of 232 respondents (55% response rate), demonstrated a good level of knowledge on genomic medicine and PGX, with no significant variation of the level of knowledge across the five academic years. A nuanced range of attitudes, encompassing both negative and positive perspectives towards genomic medicine and PGX was observed varying according to the specific questions posed. However, heavy concerns regarding data privacy, insurance implications, and the timing of implementation appeared. The results of the study highlight a need for curriculum enhancement, acknowledging the level of knowledge while emphasizing areas for improvement in students' perspectives on genomic medicine and PGX for better advancements in future healthcare of Sri Lanka.

To plan future steps for the implementation and regulation of pharmacogenetic testing, any issue in the management of pharmacogenetic information by regulatory bodies must be identified. In this paper, an analysis of pharmacogenetic information in the summary of product characteristics (SmCPs) of drugs approved by Italian Drug Agency (AIFA) was conducted. Among 4214 SmCPs of 1063 active ingredients, 53.2% (n = 2240) included pharmacogenetic information in at least one section, most frequently for drugs in the Anatomical Therapeutic Chemical category "Antineoplastic and immunomodulatory agents". To contextualize these data in the international scenario, a pharmacogenetic level of actionability, based on AIFA SmCPs, was assigned to 608 drug/gene pairs included in FDA's "Table of Pharmacogenomic Biomarkers in Drug Labels", according to PharmGKB (The Pharmacogenomics Knowledge Base). Approximately 67% of drug/gene pairs were deemed classifiable: Based on SmCPs phrasing, for half of them the genetic testing was cataloged as "required" or "recommended" (mainly tumor somatic variants), whereas 40% as "actionable" (mostly PK/PD-related germline variants). The comparison with other regulatory agencies highlighted a discordance in the assigned pharmacogenetic levels of actionability ranging from 1% to 14%. This discrepancy may also point out the need to rethink the language used in AIFA-approved SmCPs to clarify whether a pharmacogenetic test is necessary or not and for which subjects it has been recommended. For the first time, a detailed evaluation and comparative analysis of the pharmacogenetic information on Italian SmCPs was presented, placing it in an international context and laying the groundwork for rethinking pharmacogenetic indications in AIFA-approved SmCPs.

Oprm1, the gene encoding the μ-opioid receptor, has multiple reported transcripts, with a variable 3' region and many alternative sequences encoding the C-terminus of the protein. The functional implications of this variability remain mostly unexplored, though a recurring notion is that it could be exploited by developing selective ligands with improved clinical profiles. Here, we comprehensively examined Oprm1 transcriptional variants in the murine central nervous system, using long-read RNAseq as well as spatial and single-cell transcriptomics. The results were validated with RNAscope in situ hybridization. We found a mismatch between transcripts annotated in the mouse genome (GRCm38/mm10) and the RNA-seq results. Sequencing data indicated that the primary Oprm1 transcript has a 3' terminus located on chr10:6,860,027, which is ∼ 9.5 kilobases downstream of the longest annotated exon 4 end. Long-read sequencing confirmed that the final Oprm1 exon included a 10.2 kilobase long 3' untranslated region, and the presence of the long variant was unambiguously confirmed using RNAscope in situ hybridization in the thalamus, striatum, cortex and spinal cord. Conversely, expression of the Oprm1 reference transcript or alternative transcripts of the Oprm1 gene was absent or close to the detection limit. Thus, the primary transcript of the Oprm1 mouse gene is a variant with a long 3' untranslated region, which is homologous to the human OPRM1 primary transcript and encodes the same conserved C-terminal amino acid sequence.

Jakun, a Proto-Malay subtribe from Peninsular Malaysia, is believed to have inhabited the Malay Archipelago during the period of agricultural expansion approximately 4 thousand years ago (kya). However, their genetic structure and population history remain inconclusive. In this study, we report the genome structure of a Jakun female, based on whole-genome sequencing, which yielded an average coverage of 35.97-fold. We identified approximately 3.6 million single-nucleotide variations (SNVs) and 517,784 small insertions/deletions (indels). Of these, 39,916 SNVs were novel (referencing dbSNP151), and 10,167 were nonsynonymous (nsSNVs), spanning 5674 genes. Principal Component Analysis (PCA) revealed that the Jakun genome sequence closely clustered with the genomes of the Cambodians (CAM) and the Metropolitan Malays from Singapore (SG_MAS). The ADMIXTURE analysis further revealed potential admixture from the EA and North Borneo populations, as corroborated by the results from the F3, F4, and TreeMix analyses. Mitochondrial DNA analysis revealed that the Jakun genome carried the N21a haplogroup (estimated to have occurred ~19 kya), which is commonly found among Malays from Malaysia and Indonesia. From the whole-genome sequence data, we identified 825 damaging and deleterious nonsynonymous single-nucleotide polymorphisms (nsSNVs) affecting 720 genes. Some of these variants are associated with age-related macular degeneration, atrial fibrillation, and HDL cholesterol level. Additionally, we located a total of 3310 variants on 32 core adsorption, distribution, metabolism, and elimination (ADME) genes. Of these, 193 variants are listed in PharmGKB, and 21 are nsSNVs. In summary, the genetic structure identified in the Jakun individual could enhance the mapping of genetic variants for disease-based population studies and further our understanding of the human migration history in Southeast Asia.

Spatial transcriptomics sequencing technology deepens our understanding of the diversity of cell behaviors, fates and states within complex tissue, which is often determined by the fine-tuning of regulatory network functional activities. Therefore, characterizing the functional activity within tissue space is helpful for revealing the functional features that drive spatial heterogeneity, and understanding complex biological processes. Here, we describe a database, SPathDB (http://bio-bigdata.hrbmu.edu.cn/SPathDB/), which aims to dissect the pathway-mediated multidimensional spatial heterogeneity in the context of functional activity. We manually curated spatial transcriptomics datasets and biological pathways from public data resources. SPathDB consists of 1689 868 spatial spots of 695 slices from 84 spatial transcriptome datasets of human and mouse, which involves 36 tissues, and also diseases such as cancer, and provides interactive analysis and visualization of the functional activities of 114 998 pathways across these spatial spots. SPathDB provides five flexible interfaces to retrieve and analyze pathways with highly variable functional activity across spatial spots, the distribution of pathway functional activities along pseudo-space axis, pathway-mediated spatial intercellular communications and the associations between spatial pathway functional activity and the occurrence of cell types. SPathDB will serve as a foundational resource for identifying functional features and elucidating underlying mechanisms of spatial heterogeneity.

Fangji Huangqi Decoction (FJHQD), a famous Traditional Chinese Medicine (TCM) formula, has been widely applied in improving renal function. However, the interaction of bioactives from FJHQD with the targets involved in acute renal injury (AKI) has not been elucidated yet.

A network pharmacology-based approach combined with molecular docking and in vitro and in vivo validation was performed to determine the bioactives, key targets, and potential pharmacological mechanism of FJHQD against AKI.

The model of mouse renal ischemic reperfusion was adopted to verify the curative effect of FJHQD against renal injury. FJHQD was analyzed and separated by Ultra-High performance liquid chromatography (UHPLC). Bioactives and potential targets of FJHQD, as well as AKI-related targets, were retrieved from public databases. Crucial bioactive ingredients, potential targets, and signaling pathways were acquired through bioinformatics analysis, including protein-protein interaction (PPI), as well as the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Subsequently, molecular docking was carried out to predict the combination of active compounds with core targets. Besides, in vivo and vitro experiments were conducted to verify the findings.

A total of 20 bioactive ingredients of FJHQD (top 10 positive ion and negative ion compounds) and 274 FJHQD-AKI overlaped targets were screened. Bioinformatics analysis revealed that apoptosis mediated by PI3K-AKT signaling pathway might play an important role in FJHQD against AKI. Further experiments showed that FJHQD alleviated I/R-induced renal injury and OGD/R induced TEC apoptosis by activating PI3K-AKT signaling pathway. Moreover, molecular docking suggested (9Z,12Z,14E)-16-Hydroxy-9,12,14-octadecatrienoic acid, 2-Hydroxyacetophenone, Liquiritigenin, (S)-[10]-Gingerol and Isookanin-7-O-glucoside may be potential candidate agents, among which, PIK3CA interacted with Liquiritigenin, (S)-[10]-Gingerol, Isookanin-7-O-glucoside and 2-Hydroxyacetophenone respectively. AKT1 interacted with (9Z,12Z,14E)-16-Hydroxy-9,12,14-octadecatrienoic acid and 2-Hydroxyacetophenone. Cell experiments showed that the most important ingredient of FJHQD, Liquiritigenin, could inhibit the TEC apoptosis and up-regulate PI3K-Akt signaling pathway, which further confirmed the prediction by network pharmacology strategy and molecular docking.

Our results comprehensively illustrated the bioactives, potential targets, and molecular mechanism of FJHQD against AKI. It also provided a promising strategy to uncover the scientific basis and therapeutic mechanism of TCM formulae in treating diseases.

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.

Non-small cell lung cancer (NSCLC) is the most prevalent type of lung cancer the mutational spectrum of which has been extensively characterized. Treatment of patients with NSCLC based on their molecular profile is now part of the standard clinical care. The aim of this study was firstly to investigate two different NGS-based tumor profile genetic tests and secondly to assess the clinical actionability of the mutations and their association with survival and clinicopathological characteristics. Overall, 52 mutations were identified in 31 patients by either one or both assays. The most frequently mutated genes were TP53 (40.4%), KRAS (13.46%) and EGFR (9.62%). TP53 and KRAS mutations were associated with worst overall survival while KRAS was positively correlated with adenocarcinoma. The two methods showed a high concordance for the commonly covered genomic regions (97.14%). Ten mutations were identified in a genomic region exclusively covered by the MEDICOVER Genetics custom tumor profile assay. Likewise, one MET mutation was identified by the Ion Amliseq assay in a genomic region exclusively covered by Ion Amliseq. In conclusion both assays showed highly similar results in the commonly covered genomic areas, however, the MEDICOVER Genetics assay identified additional clinically actionable mutations that can be applied in clinical practice for personalized treatment decision making for patients with NSCLC.

Adverse drug reactions (ADRs) pose substantial public health issues, necessitating population-specific characterization due to variations in pharmacogenes. This study delineates the pharmacogenomic (PGx) landscape of the South Korean (SKR) population, focusing on 21 core pharmacogenes. Whole genome sequencing (WGS) was conducted on 396 individuals, including 99 healthy volunteers, 95 patients with chronic diseases, 81 with colon cancer, 81 with breast cancer, and 40 with gastric cancer, to identify genotype-specific drug dosing recommendations. Our detailed analysis, utilizing high-throughput genotyping (HTG) of CYP2D6 and comparative data from the 1,000 Genomes Project (1 KG) and the US National Marrow Donor Program (NMDP), revealed significant pharmacogenetic diversity in core pharmacogenes such as CYP2B6, CYP2C19, CYP4F2, NUDT15, and CYP2D6. Notably, intermediate metabolizer frequencies for CYP2B6 in SKR (3.28%) were comparable to Europeans (5.77%) and East Asians (5.36%) but significantly differed from other global populations (p < 0.01). For CYP2C19, 48.74% of SKR individuals were classified as intermediate metabolizers, with the *35 allele (2.02%) being unique to SKR, the allele not observed in other East Asian populations. Additionally, the high-risk *3 allele in CYP4F2 was significantly more frequent in SKR (34.72%) than in other East Asian populations (p < 0.01). NUDT15 poor metabolizers were found in 0.76% of SKR, aligning closely with other East Asians (1.59%), while TPMT poor metabolizers were predominantly observed in Europeans and Africans, with one case in SKR. We identified significant allele frequency differences in CYP2D6 variants rs1065852 and rs1135840. Among the 72 drugs analyzed, 93.43% (n = 370) of patients required dosage adjustments for at least one drug, with an average of 4.5 drugs per patient. Moreover, 31.31% (n = 124) required adjustments for more than five drugs. These findings reveal the substantial pharmacogenetic diversity of the SKR population within the global population, emphasizing the urgency of integrating population-specific PGx data into clinical practice to ensure safe and effective drug therapies. This comprehensive PGx profiling in SKR not only advances personalized medicine but also holds the potential to significantly improve healthcare outcomes on a broader scale.

ObjectivesSignificant progress has been made in the treatment of patients with pulmonary adenocarcinoma (ADCA) based on molecular profiling. However, no such molecular target exists for squamous cell carcinoma (SQCC). An exome sequence may provide new markers for personalized medicine for lung cancer patients of all subtypes. The current study aims to discover new genetic markers that can be used as universal biomarkers for non-small cell lung cancer (NSCLC).MethodsWES of 19 advanced NSCLC patients (10 ADCA and 9 SQCC) was performed using Illumina HiSeq 2000. Variant calling was performed using GATK HaplotypeCaller and then the impacts of variants on protein structure or function were predicted using SnpEff and ANNOVAR. The clinical impact of somatic variants in cancer was assessed using cancer archives. Somatic variants were further prioritized using a knowledge-driven variant interpretation approach. Sanger sequencing was used to validate functionally important variants.ResultsWe identified 24 rare single-nucleotide variants (SNVs) including 17 non-synonymous SNVs, and 7 INDELs in 18 genes possibly linked to lung carcinoma. Variants were classified as known somatic (n = 10), deleterious (n = 8), and variant of uncertain significance (n = 6). We found TBP and MPRIP genes exclusively associated with ADCA subtypes, FBOX6 with SQCC subtypes and GPRIN2, KCNJ18 and TEKT4 genes mutated in all the patients. The Sanger sequencing of 10 high-confidence somatic SNVs showed 100% concordance in 7 genes, and 80% concordance in the remaining 3 genes.ConclusionsOur bioinformatics analysis identified KCNJ18, GPRIN2, TEKT4, HRNR, FOLR3, ESSRA, CTBP2, MPRIP, TBP, and FBXO6 may contribute to progression in NSCLC and could be used as new biomarkers for the treatment. The mechanism by which GPRIN2, KCNJ12, and TEKT4 contribute to tumorigenesis is unclear, but our results suggest they may play an important role in NSCLC and it is worth investigating in future.

Mendelian randomization (MR) has been used to identify drug targets in many conditions. Height is a classic complex trait affected by genetic and early-life environmental factors. No systematic screening has been conducted to identify drugs that interact with height. We investigated the causal relationship between genes and height, and systematically screened for interactive drugs that may promote or delay growth.

We performed MR using summary statistics from the Genetic Investigation of ANthropometric Traits consortium (N=253,288), the UK Biobank (N=461,950), and the BioBank Japan Project (N=159,095). Gene expression-single-nucleotide polymorphism associations represented by cis-expression quantitative trait loci data were obtained from the Genotype-Tissue Expression study and were used as genetic instruments. We performed annotation and enrichment analyses of the genes. Interactive drugs were identified through drug-gene interactions.

Of the 27,094 genes screened, 209 had causal associations with height, including genes associated with height and short stature phenotypes (AMZ1, GNA12, NPPC, UQCC1, and ZBTB38), genes associated with height in a few studies (ANKIB1, CEP250, DCAF16, HIST1H4E, and HLA-C), and genes without previous evidence (BTN2A2 and RBMS1P1). Enrichment analysis showed that transcriptional regulation by RUNX1 was the most enriched pathway. Interactive drugs were identified, including amoxicillin, atenolol, infliximab, colchicine, propionyl-L-carnitine, BMN-111, and tamoxifen, which were known to have a positive effect on height. We also identified drugs that had a negative effect on height, including antineoplastic drugs, corticosteroids, and antiepileptic drugs. Moreover, many interactive drugs have not been previously reported to be associated with height.

Our results suggest that many genes have causal effects on height. By interrogating drug-gene interactions, interactive drugs have been identified as having both positive and negative effects on growth, which would help make clinical decisions.

The multi-targets and multi-components of Traditional Chinese medicine (TCM) coincide with the complex pathogenesis of depression. Zhi-Zi-Hou-Pu Decoction (ZZHPD) has been approved in clinical medication with good antidepression effects for centuries, while the mechanisms under the iceberg haven't been addressed systematically. This study explored its inner active ingredients - potent pharmacological mechanism - DDI to explore more comprehensively and deeply understanding of the complicated TCM in treatment.

This research utilized network pharmacology combined with molecular docking to identify pharmacological targets and molecular interactions between ZZHPD and depression. Verification of major active compounds was conducted through UPLC-Q-TOF-MS/MS and assays on LPS-induced neuroblastoma cells. Additionally, the DDIMDL model, a deep learning-based approach, was used to predict DDIs, focusing on serum concentration, metabolism, effectiveness, and adverse reactions.

The antidepressant mechanisms of ZZHPD involve the serotonergic synapse, neuroactive ligand-receptor interaction, and dopaminergic synapse signaling pathways. Eighteen active compounds were identified, with honokiol and eriocitrin significantly modulating neuronal inflammation and promoting differentiation of neuroimmune cells through genes like COMT, PI3KCA, PTPN11, and MAPK1. DDI predictions indicated that eriocitrin's serum concentration increases when combined with hesperidin, while hesperetin's metabolism decreases with certain flavonoids. These findings provide crucial insights into the nervous system's effectiveness and potential cardiovascular or nervous system adverse reactions from core compound combinations.

This study provides insights into the TCM interpretation, drug compatibility or combined medication for further clinical application or potential drug pairs with a cost-effective method of integrated network pharmacology and deep learning.

Confusion regarding methadone metabolism exists, hampering optimal clinical use. A systematic review was conducted to assess the impacts of cytochrome P450 (CYP) genetic polymorphisms on methadone outcomes.

MEDLINE, EMBASE, Web of Science, PsycINFO, and CENTRAL were searched to identify studies reporting methadone dose-adjusted plasma concentrations, clearance, maintenance dose, or treatment response in relation to CYP polymorphisms in humans. ROBINS-I was used to evaluate risk of bias in included studies. Each outcome was synthesized for each CYP using the ratio of means or odds ratio as the effect size measure.

Ten, two, fourteen, and five studies were included in the meta-analyses of the concentration, clearance, dose, and treatment response, respectively. The CYP2B6 c.516 G>T variant was robustly associated with (S)-methadone concentrations (GT+TTvs.GG: ratio of means (RoM) 1.40, p < 0.01) and clearance (GT+TTvs.GG: RoM 0.65, p < 0.01) but less with (R)- or (R,S)-methadone. The CYP2B6 variant also affected methadone dose for opioid use disorder (GT+TTvs.GG: RoM 0.93, p = 0.04). CYP2C19, CYP2C9, CYP2D6, and CYP3A5 polymorphisms did not influence any of the assessed outcomes.

CYP2B6 genetics had statistically significant impacts on (S)-methadone and less so on (R)-methadone exposure and clearance and was statistically significantly but not clinically meaningfully associated with dose requirements.

The effectiveness of drug treatments is profoundly influenced by individual responses, which are shaped by gene expression variability, particularly within pharmacogenes. Leveraging single-cell RNA sequencing (scRNA-seq) data, our study explores the extent of expression variability among pharmacogenes in a wide array of cell types across eight different human tissues, shedding light on their impact on drug responses. Our findings broaden the established link between variability in pharmacogene expression and drug efficacy to encompass variability at the cellular level. Moreover, we unveil a promising approach to enhance drug efficacy prediction. This is achieved by leveraging a combination of cross-cell and cross-individual pharmacogene expression variation measurements. Our study opens avenues for more precise forecasting of drug performance, facilitating tailored and more effective treatments in the future.

Xenobiotics, including pharmaceutical drugs, can be metabolized by both host and microbiota, in some cases by homologous enzymes. We conducted a systematic search for all human proteins with gut microbial homologs. Because gene fusion and fission can obscure homology detection, we built a pipeline to identify not only full-length homologs, but also cases where microbial homologs were split across multiple adjacent genes in the same neighborhood or operon ("split homologs"). We found that human proteins with full-length gut microbial homologs disproportionately participate in xenobiotic metabolism. While this included many different enzyme classes, short-chain and aldo-keto reductases were the most frequently detected, especially in prevalent gut microbes, while cytochrome P450 homologs were largely restricted to lower-prevalence facultative anaerobes. In contrast, human proteins with split homologs tended to play roles in central metabolism, especially of nucleobase-containing compounds. We identify twelve specific drugs that gut microbial split homologs may metabolize; two of these, 6-mercaptopurine by xanthine dehydrogenase (XDH) and 5-fluorouracil by dihydropyrimidine dehydrogenase (DPYD), have been recently confirmed in mouse models. This work provides a comprehensive map of homology between the human and gut microbial proteomes, indicates which human xenobiotic enzyme classes are most likely to be shared by gut microorganisms, and finally demonstrates that split homology may be an underappreciated explanation for microbial contributions to drug metabolism.

Precisely predicting Drug-Drug Interactions (DDIs) carries the potential to elevate the quality and safety of drug therapies, protecting the well-being of patients, and providing essential guidance and decision support at every stage of the drug development process. In recent years, leveraging large-scale biomedical knowledge graphs has improved DDI prediction performance. However, the feature extraction procedures in these methods are still rough. More refined features may further improve the quality of predictions. To overcome these limitations, we develop a knowledge graph-based method for multi-typed DDI prediction with contrastive learning (KG-CLDDI). In KG-CLDDI, we combine drug knowledge aggregation features from the knowledge graph with drug topological aggregation features from the DDI graph. Additionally, we build a contrastive learning module that uses horizontal reversal and dropout operations to produce high-quality embeddings for drug-drug pairs. The comparison results indicate that KG-CLDDI is superior to state-of-the-art models in both the transductive and inductive settings. Notably, for the inductive setting, KG-CLDDI outperforms the previous best method by 17.49% and 24.97% in terms of AUC and AUPR, respectively. Furthermore, we conduct the ablation analysis and case study to show the effectiveness of KG-CLDDI. These findings illustrate the potential significance of KG-CLDDI in advancing DDI research and its clinical applications.

 Venous thromboembolism (VTE) is a chronic disorder with a significant health and economic burden. Several VTE-specific clinical prediction models (CPMs) have been used to assist physicians in decision-making but have several limitations. This systematic review explores if machine learning (ML) can enhance CPMs by analyzing extensive patient data derived from electronic health records. We aimed to explore ML-CPMs' applications in VTE for risk stratification, outcome prediction, diagnosis, and treatment.

 Three databases were searched: PubMed, Google Scholar, and IEEE electronic library. Inclusion criteria focused on studies using structured data, excluding non-English publications, studies on non-humans, and certain data types such as natural language processing and image processing. Studies involving pregnant women, cancer patients, and children were also excluded. After excluding irrelevant studies, a total of 77 studies were included.

 Most studies report that ML-CPMs outperformed traditional CPMs in terms of receiver operating area under the curve in the four clinical domains that were explored. However, the majority of the studies were retrospective, monocentric, and lacked detailed model architecture description and external validation, which are essential for quality audit. This review identified research gaps and highlighted challenges related to standardized reporting, reproducibility, and model comparison.

 ML-CPMs show promise in improving risk assessment and individualized treatment recommendations in VTE. Apparently, there is an urgent need for standardized reporting and methodology for ML models, external validation, prospective and real-world data studies, as well as interventional studies to evaluate the impact of artificial intelligence in VTE.

Traditional treatment strategies for recurrent pregnancy loss (RPL) and recurrent implantation failure (RIF) often result in limited success, placing significant emotional and financial burdens on couples. However, novel approaches such as diagnostic gene profiling, cell therapy, stem cell-derived exosome therapy, and pharmacogenomics offer promising, personalized treatments. Combining traditional treatments with precision and regenerative medicine may enhance the efficacy of these approaches and improve pregnancy outcomes. This review explores how integrating these strategies can potentially transform the lives of couples experiencing repeated pregnancy loss or implantation failure, providing hope for improved treatment success. Precision and regenerative medicine represent a new frontier for managing RPL and RIF, offering promising solutions.

Tacrolimus (TAC) is a commonly used immunosuppressive drug in solid organ transplantation. Pharmacogenetics has been demonstrated before to be decisive in TAC pharmacotherapy. The CYP3A5*3 variant has been reported to be the main determinant of TAC dose requirements; however, other polymorphisms have also proven to be influential, especially in CYP3A5 non-expressor patients. The aim of this study is to evaluate the influence of genetic polymorphisms in TAC therapy in a cohort of Spanish transplant recipients. Genetic analysis including ten polymorphic variants was performed, and demographic and clinical data and pharmacotherapy of 26 patients were analyzed. No significant differences were found in weight-adjusted dose between CYP3A5 expressors and non-expressors (0.047 mg/kg vs. 0.044 mg/kg), while they were found for carriers of the CYP3A4*1B allele (0.101 mg/kg; p < 0.05). The results showed that patients with at least one CYP3A4*1B allele had a higher TAC dose and lower blood concentration. Dose-adjusted TAC blood levels were also lower in CYP3A4*1B carriers compared to non-carriers (0.72 ng/mL/mg vs. 2.88 ng/mL/mg). These results support the independence of CYP3A5*3 and CYP3A4*1B variants as determinants of dose requirements despite the linkage disequilibrium present between the two. The variability in genotype frequency between ethnicities may be responsible for the discrepancy found between studies.

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.

Pharmacogenetics (PGx) holds promise for optimizing psychotropic medication use, with CYP2D6 and CYP2C19 identified as key genes in antidepressant treatment. However, few studies have explored the genetic variants of these genes in real-world settings for patients experiencing ineffectiveness or adverse drug reactions (ADRs) to antidepressants.

This case series includes 40 patients who underwent PGx testing due to antidepressant ineffectiveness or ADRs between June 2020 and April 2022. We describe the patients' demographic, clinical, and genetic characteristics and assess the value of PGx testing based on feedback from their psychiatrists.

The most common diagnoses were major depressive disorder (60.0%) and post-traumatic stress disorder (30.0%). Ineffectiveness was reported in 65.0% of patients, ADRs in 2.5%, and both in 32.5%. The antidepressants involved included SSRIs (45.0%), SNRIs (27.5%), atypical antidepressants (20.0%), and tricyclics (17.5%). Only 17.5% of patients had normal CYP2D6 and CYP2C19 metabolic activity. Actionable genetic variants were identified in 22.0% of CYP2D6/CYP2C19-antidepressant-response pairs. PGx recommendations were followed in 92.7% of cases, with significant improvement in ADRs reported in 71.4% of patients and efficacy improvement in 79.5%.

Our findings suggest that PGx testing can guide prescribing decisions for patients with antidepressant ineffectiveness or ADRs. The relatively high prevalence of genetic variants affecting pharmacokinetics supports the broader adoption of PGx testing in psychiatric practice.

Identification of active components of traditional Chinese Medicine (TCM) and their respective targets is important for understanding the mechanisms underlying TCM efficacy. However, there are still no effective technical methods to achieve this.

Herein, we have established a method for rapidly identifying targets of a specific TCM and interrogating the targets with their corresponding active components based on Isothermal Shift Assay (iTSA) and database interrogation.

We optimized iTSA workflow and identified 110 targets for Danhong injection (DHI) which is used as an effective remedy for cardiovascular and cerebrovascular diseases. Moreover, we identified the targets of the nine major ingredients found in DHI. Database interrogation found that the potential targets for DHI, in which we verified that ADK as the target for salvianolic acid A and ALDH1B1 as the target for protocatechualdehyde in DHI, respectively.

Overall, we established a novel paradigm model for the identification of targets and their respective ingredients in DHI, which facilitates the discovery of drug candidates and targets for improving disease management and contributes to revealing the underlying mechanisms of TCM and fostering TCM development and modernization.

The clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9) is a genome editing tool widely used in biological research and clinical therapeutics. Natural human genetic variations, through altering the sequence context of CRISPR-Cas9 target regions, can significantly affect its DNA repair outcomes and ultimately lead to different editing efficiencies. However, these effects have not been systematically studied, even as CRISPR-Cas9 is broadly applied to primary cells and patient samples that harbor such genetic diversity. Here, we present comprehensive investigations of natural genetic variations on CRISPR-Cas9 outcomes across the human genome. The utility of our analysis is illustrated in two case studies, on both preclinical discoveries of CD33 knockout in chimeric antigen receptor-T cell therapy and clinical applications of transthyretin (TTR) inactivation for treating TTR amyloidosis. We further expand our analysis to genome-scale, population-stratified common variants that may lead to gene editing disparity. Our analyses demonstrate pitfalls of failing to account for the widespread genetic variations in Cas9 target selection and how they can be effectively examined and avoided using our method. To facilitate broad access to our analysis, a web platform CROTONdb is developed, which provides predictions for all possible CRISPR-Cas9 target sites in the coding and noncoding regulatory regions, spanning over 5.38 million guide RNA targets and 90.82 million estimated variant effects. We anticipate CROTONdb having broad clinical utilities in gene and cellular therapies.

With the continuous development of genetics in healthcare, there has been a significant contribution to the development of precision medicine, which is ultimately aimed at improving the care of patients. Generally, drug treatments used in Oncology are characterized by a narrow therapeutic range and by their potential toxicity. Knowledge of pharmacogenomics and pharmacogenetics can be very useful in the area of Oncology, as they constitute additional tools that can help to individualize patients' treatment. This work includes a description of some genes that have been revealed to be useful in the field of Oncology, as they play a role in drug prescription and in the prediction of treatment response.

Based on data from the Global Cancer Statistics 2022, lung cancer stands as the most lethal cancer worldwide, with age-adjusted incidence and mortality rates of 23.6 and 16.9 per 100,000 people, respectively. Despite significant strides in precision oncology driven by large-scale international research consortia, there remains a critical need to deepen our understanding of the genomic landscape across diverse racial and ethnic groups. To address this challenge, we performed comprehensive in silico analyses and data mining to identify pathogenic variants in genes that drive lung cancer. We subsequently calculated the allele frequencies and assessed the deleteriousness of these oncogenic variants among populations such as African, Amish, Ashkenazi Jewish, East and South Asian, Finnish and non-Finnish European, Latino, and Middle Eastern. Our analysis examined 117,707 variants within 86 lung cancer-associated genes across 75,109 human genomes, uncovering 8042 variants that are known or predicted to be pathogenic. We prioritized variants based on their allele frequencies and deleterious scores, and identified those with potential significance for response to anti-cancer therapies through in silico drug simulations, current clinical pharmacogenomic guidelines, and ongoing late-stage clinical trials targeting lung cancer-driving proteins. In conclusion, it is crucial to unite global efforts to create public health policies that emphasize prevention strategies and ensure access to clinical trials, pharmacogenomic testing, and cancer research for these groups in developed nations.

Only a handful of somatic alterations have been linked to endocrine therapy resistance in hormone-dependent breast cancer, potentially explaining ∼40% of relapses. If other mechanisms underlie the evolution of hormone-dependent breast cancer under adjuvant therapy is currently unknown. In this work, we employ functional genomics to dissect the contribution of cis-regulatory elements (CRE) to cancer evolution by focusing on 12 megabases of noncoding DNA, including clonal enhancers, gene promoters, and boundaries of topologically associating domains. Parallel epigenetic perturbation (CRISPRi) in vitro reveals context-dependent roles for many of these CREs, with a specific impact on dormancy entrance and endocrine therapy resistance. Profiling of CRE somatic alterations in a unique, longitudinal cohort of patients treated with endocrine therapies identifies a limited set of noncoding changes potentially involved in therapy resistance. Overall, our data uncover how endocrine therapies trigger the emergence of transient features which could ultimately be exploited to hinder the adaptive process. Significance: This study shows that cells adapting to endocrine therapies undergo changes in the usage or regulatory regions. Dormant cells are less vulnerable to regulatory perturbation but gain transient dependencies which can be exploited to decrease the formation of dormant persisters.

The human cytochrome P450 (CYP) 1, 2 and 3 families of enzymes are responsible for the biotransformation of a majority of the currently available pharmaceutical drugs. The highly polymorphic CYP2C9 predominantly metabolizes many drugs including anticoagulant S-warfarin, anti-hypertensive losartan, anti-diabetic tolbutamide, analgesic ibuprofen, etc. There are >80 single nucleotide changes identified in CYP2C9, many of which significantly alter the clearance of important drugs. Here we report the structural and biophysical analysis of two polymorphic variants, CYP2C9*14 (Arg125His) and CYP2C9*27 (Arg150Leu) complexed with losartan. The X-ray crystal structures of the CYP2C9*14 and *27 illustrate the binding of two losartan molecules, one in the active site near heme and another on the periphery. Both losartan molecules are bound in an identical conformation to that observed in the previously solved CYP2C9 wild-type complex, however, the number of losartan differs from the wild-type structure, which showed binding of three molecules. Additionally, isothermal titration calorimetry experiments reveal a lower binding affinity of losartan with *14 and *27 variants when compared to the wild-type. Overall, the results provide new insights into the effects of these genetic polymorphisms and suggests a possible mechanism contributing to reduced metabolic activity in patients carrying these alleles.