Applications of L-Arginine in Pregnancy and Beyond: An Emerging Pharmacogenomic Approach.

L-arginine is a semi-essential amino acid that plays a critical role in various physiological processes, such as protein synthesis, wound healing, immune function, and cardiovascular regulation. The use of L-arginine in pregnancy has been an emerging topic in the field of pharmacogenomics. L-arginine, an amino acid, plays a crucial role in the production of nitric oxide, which is necessary for proper placental development and fetal growth. Studies have shown that L-arginine supplementation during pregnancy can have positive effects on fetal growth, maternal blood pressure, and the prevention of preeclampsia. This emerging pharmacogenomic approach involves using genetic information to personalize L-arginine dosages for pregnant women based on their specific genetic makeup. By doing so, it may be possible to optimize the benefits of L-arginine supplementation during pregnancy and improve pregnancy outcomes. This paper emphasizes the potential applications of L-arginine in pregnancy and the use of pharmacogenomic approaches to enhance its effectiveness. Nonetheless, the emerging pharmacogenomic approach to the application of L-arginine offers exciting prospects for the development of novel therapies for a wide range of diseases.

Machine Learning and Pharmacogenomics at the Time of Precision Psychiatry.

Traditional medicine and biomedical sciences are reaching a turning point because of the constantly growing impact and volume of Big Data. Machine Learning (ML) techniques and related algorithms play a central role as diagnostic, prognostic, and decision-making tools in this field. Another promising area becoming part of everyday clinical practice is personalized therapy and pharmacogenomics. Applying ML to pharmacogenomics opens new frontiers to tailored therapeutical strategies to help clinicians choose drugs with the best response and fewer side effects, operating with genetic information and combining it with the clinical profile. This systematic review aims to draw up the state-of-the-art ML applied to pharmacogenomics in psychiatry. Our research yielded fourteen papers; most were published in the last three years. The sample comprises 9,180 patients diagnosed with mood disorders, psychoses, or autism spectrum disorders. Prediction of drug response and prediction of side effects are the most frequently considered domains with the supervised ML technique, which first requires training and then testing. The random forest is the most used algorithm; it comprises several decision trees, reduces the training set's overfitting, and makes precise predictions. ML proved effective and reliable, especially when genetic and biodemographic information were integrated into the algorithm. Even though ML and pharmacogenomics are not part of everyday clinical practice yet, they will gain a unique role in the next future in improving personalized treatments in psychiatry.

Genetic Influence on Treatment Response in Psoriasis: New Insights into Personalized Medicine.

Psoriasis is a chronic inflammatory disease with an established genetic background. The HLA-Cw*06 allele and different polymorphisms in genes involved in inflammatory responses and keratinocyte proliferation have been associated with the development of the disease. Despite the effectiveness and safety of psoriasis treatment, a significant percentage of patients still do not achieve adequate disease control. Pharmacogenetic and pharmacogenomic studies on how genetic variations affect drug efficacy and toxicity could provide important clues in this respect. This comprehensive review assessed the available evidence for the role that those different genetic variations may play in the response to psoriasis treatment. One hundred fourteen articles were included in this qualitative synthesis. VDR gene polymorphisms may influence the response to topical vitamin D analogs and phototherapy. Variations affecting the ABC transporter seem to play a role in methotrexate and cyclosporine outcomes. Several single-nucleotide polymorphisms affecting different genes are involved with anti-TNF-α response modulation (TNF-α, TNFRSF1A, TNFRSF1B, TNFAIP3, FCGR2A, FCGR3A, IL-17F, IL-17R, and IL-23R, among others) with conflicting results. HLA-Cw*06 has been the most extensively studied allele, although it has only been robustly related to the response to ustekinumab. However, further research is needed to firmly establish the usefulness of these genetic biomarkers in clinical practice.

Identification of Cumin (Cuminum cyminum) MicroRNAs through Deep Sequencing and Their Impact on Plant Secondary Metabolism.

The pharmacological properties of plants lie in the content of secondary metabolites that are classified into different categories based on their biosynthesis, structures, and functions. MicroRNAs (miRNAs) are small non-coding RNA molecules that play crucial post-transcriptional regulatory roles in plants, including development and stress-response signaling; however, information about their involvement in secondary metabolism is still limited. Cumin is one of the most popular seeds from the plant Cuminum cyminum, with extensive applications in herbal medicine and cooking; nevertheless, no previous studies focus on the miRNA profile of cumin. In this study, the miRNA profile of C. cyminum and its association with the biosynthesis of secondary metabolites were determined using NGS technology. The sequencing data yielded 10,956,054 distinct reads with lengths ranging from 16 to 40 nt, of which 349 miRNAs were found to be conserved and 39 to be novel miRNAs. Moreover, this work identified 1959 potential target genes for C. cyminum miRNAs. It is interesting to note that several conserved and novel miRNAs have been found to specifically target important terpenoid backbone, flavonoid biosynthesis, and lipid/fatty acid pathways enzymes. We believe this investigation will aid in elucidating the implications of miRNAs in plant secondary metabolism.

Pediatric considerations for pharmacogenetic selective serotonin reuptake inhibitors clinical decision support.

Pharmacogenetic testing for psychiatry is growing at a rapid pace, with multiple sites utilizing results to help clinical decision-making. Genotype-guided dosing and drug selection have been implemented at several sites, including Vanderbilt University Medical Center, where clinical decision support (CDS) based on pharmacogenetic results went live for selective serotonin reuptake inhibitors in 2020 for both adult and pediatric patients. Effective and appropriate implementation of CYP2D6- and CYP2C19-guided CDS for the pediatric population requires consideration of the evidence for the pharmacogenetic associations, medication indications, and appropriate alternative therapies to be used when a pharmacogenetic contraindication is identified. In this article, we review these pediatric pharmacogenetic considerations for selective serotonin reuptake inhibitor CDS. We include a case study, the current literature supporting clinical recommendations, considerations when designing pediatric CDS, future implications, and examples of sertraline, (es)citalopram, paroxetine, and fluvoxamine alerts.

Advancing Precision Medicine in Paediatrics: Past, present and future.

Precision Medicine is an approach to disease treatment and prevention taking into account individual genetic, environmental, therapeutic and lifestyle variability for each person. This holistic approach to therapeutics is intended to enhance drug efficacy and safety not only across healthcare systems but for individual patients. While weight and to some extent gestational age have been considered in determining drug dosing in children, historically other factors including genetic variability have not been factored into therapeutic decision making. As our knowledge of the role of ontogeny and genetics in determining drug efficacy and safety has expanded, these insights have provided new opportunities to apply principles of Precision Medicine to the care of infants, children and youth. These opportunities are most likely to be achieved first in select sub-groups of children. While there are many challenges to the successful implementation of Precision Medicine in children including the need to ensure that Precision Medicine enhances rather than reduces equity in children's health care rather, there are many more opportunities. Research, advocacy, planning and teamwork are required to move Precision Medicine forward in children in pursuit of the common goal of safe and effective drug therapy.

Pharmacological Mechanism of NRICM101 for COVID-19 Treatments by Combined Network Pharmacology and Pharmacodynamics.

Symptom treatments for Coronavirus disease 2019 (COVID-19) infection and Long COVID are one of the most critical issues of the pandemic era. In light of the lack of standardized medications for treating COVID-19 symptoms, traditional Chinese medicine (TCM) has emerged as a potentially viable strategy based on numerous studies and clinical manifestations. Taiwan Chingguan Yihau (NRICM101), a TCM designed based on a medicinal formula with a long history of almost 500 years, has demonstrated its antiviral properties through clinical studies, yet the pharmacogenomic knowledge for this formula remains unclear. The molecular mechanism of NRICM101 was systematically analyzed by using exploratory bioinformatics and pharmacodynamics (PD) approaches. Results showed that there were 434 common interactions found between NRICM101 and COVID-19 related genes/proteins. For the network pharmacology of the NRICM101, the 434 common interacting genes/proteins had the highest associations with the interleukin (IL)-17 signaling pathway in the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Moreover, the tumor necrosis factor (TNF) was found to have the highest association with the 30 most frequently curated NRICM101 chemicals. Disease analyses also revealed that the most relevant diseases with COVID-19 infections were pathology, followed by cancer, digestive system disease, and cardiovascular disease. The 30 most frequently curated human genes and 2 microRNAs identified in this study could also be used as molecular biomarkers or therapeutic options for COVID-19 treatments. In addition, dose-response profiles of NRICM101 doses and IL-6 or TNF-α expressions in cell cultures of murine alveolar macrophages were constructed to provide pharmacodynamic (PD) information of NRICM101. The prevalent use of NRICM101 for standardized treatments to attenuate common residual syndromes or chronic sequelae of COVID-19 were also revealed for post-pandemic future.

Pharmacogenomics driven decision support prototype with machine learning: A framework for improving patient care.

Introduction: A growing number of healthcare providers make complex treatment decisions guided by electronic health record (EHR) software interfaces. Many interfaces integrate multiple sources of data (e.g., labs, pharmacy, diagnoses) successfully, though relatively few have incorporated genetic data. Method: This study utilizes informatics methods with predictive modeling to create and validate algorithms to enable informed pharmacogenomic decision-making at the point of care in near real-time. The proposed framework integrates EHR and genetic data relevant to the patient's current medications including decision support mechanisms based on predictive modeling. We created a prototype with EHR and linked genetic data from the Department of Veterans Affairs (VA), the largest integrated healthcare system in the US. The EHR data included diagnoses, medication fills, and outpatient clinic visits for 2,600 people with HIV and matched uninfected controls linked to prototypic genetic data (variations in single or multiple positions in the DNA sequence). We then mapped the medications that patients were prescribed to medications defined in the drug-gene interaction mapping of the Clinical Pharmacogenomics Implementation Consortium's (CPIC) level A (i.e., sufficient evidence for at least one prescribing action) guidelines that predict adverse events. CPIC is a National Institute of Health funded group of experts who develop evidence based pharmacogenomic guidelines. Preventable adverse events (PAE) can be defined as a harmful outcome from an intervention that could have been prevented. For this study, we focused on potential PAEs resulting from a medication-gene interaction. Results: The final model showed AUC scores of 0.972 with an F1 score of 0.97 with genetic data as compared to 0.766 and 0.73 respectively, without genetic data integration. Discussion: Over 98% of people in the cohort were on at least one medication with CPIC level a guideline in their lifetime. We compared predictive power of machine learning models to detect a PAE between five modeling methods: Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), K Nearest neighbors (KNN), and Decision Tree. We found that XGBoost performed best for the prototype when genetic data was added to the framework and improved prediction of PAE. We compared area under the curve (AUC) between the models in the testing dataset.

The Yin-Yang Dynamics in Cancer Pharmacogenomics and Personalized Medicine.

The enormous heterogeneity of cancer systems has made it very challenging to overcome drug resistance and adverse reactions to achieve personalized therapies. Recent developments in systems biology, especially the perception of cancer as the complex adaptive system (CAS), may help meet the challenges by deciphering the interactions at various levels from the molecular, cellular, tissue-organ, to the whole organism. The ubiquitous Yin-Yang interactions among the coevolving components, including the genes and proteins, decide their spatiotemporal features at various stages from cancer initiation to metastasis. The Yin-Yang imbalances across different systems levels, from genetic mutations to tumor cells adaptation, have been related to the intra- and inter-tumoral heterogeneity in the micro- and macro-environments. At the molecular and cellular levels, dysfunctional Yin-Yang dynamics in the cytokine networks, mitochondrial activities, redox systems, apoptosis, and metabolism can contribute to tumor cell growth and escape of immune surveillance. Up to the organism and system levels, the Yin-Yang imbalances in the cancer microenvironments can lead to different phenotypes from breast cancer to leukemia. These factors may be considered the systems-based biomarkers and treatment targets. The features of adaptation and nonlinearity in Yin-Yang dynamical interactions should be addressed by individualized drug combinations, dosages, intensities, timing, and frequencies at different cancer stages. The comprehensive "Yin-Yang dynamics" framework would enable powerful approaches for personalized and systems medicine strategies.

The Yin-Yang Dynamics in Cardiovascular Pharmacogenomics and Personalized Medicine.

Studies of genetic variants and systems biology have indicated that Yin-Yang dynamics are especially meaningful for cardiovascular pharmacogenomics and personalized therapeutic strategies. The comprehensive concepts of Yin-Yang can be used to characterize the dynamical factors in the adaptive microenvironments of the complex cardiovascular systems. The Yin-Yang imbalances in the complex adaptive systems (CAS) at different levels and stages are essential for cardiovascular diseases (CVDs), including atherosclerosis, hypertension, and heart failure (HF). At the molecular and cellular levels, Yin-Yang interconnections have been considered critical for genetic variants and various pathways, mitophagy, cell death, and cholesterol homeostasis. The significance of the adaptive and spatiotemporal factors in the nonlinear Yin-Yang interactions has been identified in different pathophysiological processes such as fibrosis. The Yin-Yang dynamical balances between proinflammatory and anti-inflammatory cytokines have vital roles in the complex reactions to stress and impairments to the heart. Procoagulant and anticoagulant lipids and lipoproteins in plasma have the Yin-Yang roles that increase or decrease thrombin productions and thrombosis. At the systems level, the Yin-Yang type of relationships has been suggested between atrial fibrillation (AF), diastolic dysfunction (DD), and HF. Based on such perceptions, systemic and personalized cardiovascular profiles can be constructed by embracing the features of CAS, especially the microenvironments and the adaptative pathophysiological stages. These features can be integrated into the comprehensive Yin-Yang dynamics framework to identify more accurate biomarkers for better prevention and treatments. The goal of reestablishing ubiquitous Yin-Yang dynamical balances may become the central theme for personalized and systems medicine for cardiovascular diseases.

Towards Personalized Medicine in Psoriasis: Current Progress.

Although innovative targeted therapies have positively revolutionized psoriasis treatment shifting treatment goals to complete or almost complete skin clearance, primary or secondary lack of efficacy is still possible. Hence, identifying robust biomarkers that reflect the various clinical psoriasis phenotypes would allow stratify patients in subgroups or endotypes, and tailor treatments according to the characteristics of each individual (precision medicine). To sum up the current progress in personalized medicine for psoriasis, we performed a review on the available evidence on biomarkers predictive of response to psoriasis treatments, with focus on phototherapy and systemic agents. Relevant literature published in English was searched for using the following databases from the last five years up to March 20, 2022: PubMed, Embase, Google Scholar, EBSCO, MEDLINE, and the Cochrane library. Currently, more evidence exists towards biologicals, as justified by the huge health care costs as compared to phototherapy or conventional systemic drugs. Among them, most of the studies focused on anti-TNF and IL12/23, with still few on IL17 (mainly secukinumab). The most discussed biomarker gene is the HLA-C*02:06 status that has been shown to be associated with psoriasis, and also differential response to biologicals. Although its positivity is associated with great response to MTX, debatable results were retrieved concerning both anti-TNF and IL12/23 while it seems not to affect secukinumab response. Personalized treatment in psoriasis would provide excellent outcome minimizing the risk of side effects. To date, although several candidates were proposed and assessed, the scarcity and heterogeneity of the results do not allow the identification of the gold-standard biomarker per each treatment. Anyway, the creation of a more comprehensive panel would be more reliable for the treatment decision process.

Genetic polymorphisms of pharmacogenomic VIP variants in the Lahu population from Yunnan Province.

BACKGROUND: Pharmacogenomics has been widely used to study the very important pharmacogenetic (VIP) variants among populations, but information on pharmacogenomics in the Lahu population is limited. The purpose of this study was to determine the differences in the distribution of VIP variants between the Lahu and the other 26 populations. METHODS: We genotyped 55 VIP variants of 27 genes in the Lahu population from the PharmGKB database. χ2 test was used to compare the genotype and allele frequencies between the Lahu and the other 26 populations from the 1000 Genomes Project. RESULTS: The genotype and allele frequencies of single nucleotide polymorphisms (SNPs) on rs20417 (PTGS2), rs776746 (CYP3A5), rs2115819 (ALOX5), and rs3093105 (CYP4F2) were considerably different in the Lahu population compared with those in the other 26 populations. Besides, based on the PharmGKB database, we identified several VIP variants that may alter the drug metabolism of aspirin (PTGS2), tacrolimus (CYP3A5), montelukast (ALOX5), and vitamin E (CYP4F2). CONCLUSION: The results show that there are significant differences in the genotype frequency distribution between the Lahu and the other 26 populations. Our study supplements the pharmacogenomics information of the Lahu population and provides a theoretical basis for individualized medicine in Lahu.

Interaction study of salvianolic acids for injection on pharmacokinetics of clopidogrel in rats using LC-MS/MS.

Salvianolic acids for injection (SAI) is developed from traditional Chinese medicine and approved for the treatment of cardiovascular and cerebrovascular diseases. Clopidogrel is an inhibitor of platelet aggregation, which is often prescribed for patients in combination with SAI. This present study aimed to assess the effects of SAI on the pharmacogenomics, pharmacokinetics, and pharmacodynamics of clopidogrel, thereby ensuring the safety and efficacy of coadministration. In vitro cytochrome P450 isoenzyme assays were performed in human liver microsomes using LC-MS/MS method to assess the metabolites of CYPs substrates. The effects of SAI on the pharmacokinetic and pharmacodynamic behaviors of clopidogrel were investigated in rats. The main pharmacokinetic parameters were analyzed using LC-MS/MS. Prothrombin time, activated partial thromboplastin time, bleeding time, and inhibition of platelet aggregation were measured to evaluate the effects of pharmacodynamics. Our study revealed that the clinical dose of SAI has no significant inhibitory effect on clopidogrel-related liver microsome metabolic CYP450 isoenzymes. Moreover, SAI did not affect the pharmacokinetics of clopidogrel when rats were administered both single and multiple doses. In pharmacodynamic study, SAI has no effect on platelet aggregation rate, prothrombin time, and activated partial thromboplastin time of clopidogrel but could significantly prevent the risk of bleeding caused by clopidogrel.

Integrative pharmacogenomics revealed three subtypes with different immune landscapes and specific therapeutic responses in lung adenocarcinoma.

Background: Pharmacogenomics is crucial for individualized drug therapy and plays an increasingly vital role in precision medicine decision-making. However, pharmacogenomics-based molecular subtypes and their potential clinical significance remain primarily unexplored in lung adenocarcinoma (LUAD). Methods: A total of 2065 samples were recruited from eight independent cohorts. Pharmacogenomics data were generated from the profiling of relative inhibition simultaneously in mixtures (PRISM) and the genomics of drug sensitivity in cancer (GDSC) databases. Multiple bioinformatics approaches were performed to identify pharmacogenomics-based subtypes and find subtype-specific properties. Results: Three reproducible molecular subtypes were found, which were independent prognostic factors and highly associated with stage, survival status, and accepted molecular subtypes. Pharmacogenomics-based subtypes had distinct molecular characteristics: S-Ⅰ was inflammatory, proliferative, and immune-evasion; S-Ⅱ was proliferative and genetics-driven; S-III was metabolic and methylation-driven. Finally, our study provided subtype-guided personalized treatment strategies: Immune checkpoint blockers (ICBs), doxorubicin, tipifarnib, AZ628, and AZD6244 were for S-Ⅰ; Cisplatin, camptothecin, roscovitine, and A.443654 were for S-Ⅱ; Docetaxel, paclitaxel, vinorelbine, and BIBW2992 were for S-III. Conclusion: We provided a novel molecular classification strategy and revealed three pharmacogenomics-based subtypes for LUAD patients, which uncovered potential subtype-related and patient-specific therapeutic strategies.

Evaluation of statistical approaches for association testing in noisy drug screening data.

BACKGROUND: Identifying associations among biological variables is a major challenge in modern quantitative biological research, particularly given the systemic and statistical noise endemic to biological systems. Drug sensitivity data has proven to be a particularly challenging field for identifying associations to inform patient treatment. RESULTS: To address this, we introduce two semi-parametric variations on the commonly used concordance index: the robust concordance index and the kernelized concordance index (rCI, kCI), which incorporate measurements about the noise distribution from the data. We demonstrate that common statistical tests applied to the concordance index and its variations fail to control for false positives, and introduce efficient implementations to compute p-values using adaptive permutation testing. We then evaluate the statistical power of these coefficients under simulation and compare with Pearson and Spearman correlation coefficients. Finally, we evaluate the various statistics in matching drugs across pharmacogenomic datasets. CONCLUSIONS: We observe that the rCI and kCI are better powered than the concordance index in simulation and show some improvement on real data. Surprisingly, we observe that the Pearson correlation was the most robust to measurement noise among the different metrics.

Development of the pharmacogenomics and genomics literacy framework for pharmacists.

BACKGROUND: Pharmacists play a unique role in integrating genomic medicine and pharmacogenomics into the clinical practice and to translate pharmacogenomics from bench to bedside. However, the literature suggests that the knowledge gap in pharmacogenomics is a major challenge; therefore, developing pharmacists' skills and literacy to achieve this anticipated role is highly important. We aim to conceptualize a personalized literacy framework for the adoption of genomic medicine and pharmacogenomics by pharmacists in the United Arab Emirates with possible regional and global relevance. RESULTS: A qualitative approach using focus groups was used to design and to guide the development of a pharmacogenomics literacy framework. The Health Literacy Skills framework was used as a guide to conceptualize the pharmacogenomics literacy for pharmacists. The framework included six major components with specific suggested factors to improve pharmacists' pharmacogenomics literacy. Major components include individual inputs, demand, skills, knowledge, attitude and sociocultural factors. CONCLUSION: This framework confirms a holistic bottom-up approach toward the implementation of pharmacogenomics. Personalized medicine entails personalized efforts and frameworks. Similar framework can be created for other healthcare providers, patients and stakeholders.

Cannabis Dopaminergic Effects Induce Hallucinations in a Patient with Parkinson's Disease.

Cannabis products that contain the tetrahydrocannabinol (THC) cannabinoid are emerging as promising therapeutic agents for the treatment of medical conditions such as chronic pain. THC elicits psychoactive effects through modulation of dopaminergic neurons, thereby altering levels of dopamine in the brain. This case report highlights the complexity associated with medicinal cannabis and the health risks associated with its use. A 57-year-old male with Parkinson's disease was experiencing worsening tremors and vivid hallucinations despite therapy optimization attempts. It was discovered that the patient took cannabis for chronic back pain, and a pharmacogenomics (PGx) test indicated the presence of variants for the COMT and HTR2A genes. These variants could increase dopamine levels and predispose patients to visual hallucinations. Once the cannabis was discontinued, the patient's hallucinations began to slowly dissipate. Cannabis use continues to expand as it gains more acceptance legally and medicinally, but cannabis can affect the response to drugs. This patient case suggests that cannabis use in combination with dopamine-promoting drugs, especially in a patient with genetic variants, can increase the risk for vivid hallucinations. These conditions support the importance of considering herb-drug interactions and PGx data when performing a medication safety review.

Large-scale prospective genome-wide association study of oxaliplatin in stage II/III colon cancer and neuropathy.

BACKGROUND: The severity of oxaliplatin (L-OHP)-induced peripheral sensory neuropathy (PSN) exhibits substantial interpatient variability, and some patients suffer from long-term, persisting PSN. To identify single-nucleotide polymorphisms (SNPs) predicting L-OHP-induced PSN using a genome-wide association study (GWAS) approach. PATIENTS AND METHODS: A large prospective GWAS including 1379 patients with stage II/III colon cancer who received L-OHP-based adjuvant chemotherapy (mFOLFOX6/CAPOX) under the phase II (JOIN/JFMC41) or the phase III (ACHIVE/JFMC47) trial. Firstly, GWAS comparison of worst grade PSN (grade 0/1 versus 2/3) was carried out. Next, to minimize the impact of ambiguity in PSN grading, extreme PSN phenotypes were selected and analyzed by GWAS. SNPs that could predict time to recovery from PSN were also evaluated. In addition, SNPs associated with L-OHP-induced allergic reactions (AR) and time to disease recurrence were explored. RESULTS: No SNPs exceeded the genome-wide significance (P < 5.0 × 10-8) in either GWAS comparison of worst grade PSN, extreme PSN phenotypes, or time to recovery from PSN. An association study focusing on AR or time to disease recurrence also failed to reveal any significant SNPs. CONCLUSION: Our results highlight the challenges of utilizing SNPs for predicting susceptibility to L-OHP-induced PSN in daily clinical practice.