Pharmacogenomic biomarkers for personalized medicine

Potential of integrating phytochemicals with standard treatments for enhanced outcomes in TBI

OBJECTIVE

TBI's intricate pathophysiology, which includes oxidative stress, neuroinflammation, apoptosis, and mechanical injury, makes it a serious public health concern. Although stabilization and secondary damage management are the main goals of current treatments, their efficacy is still restricted. The potential for improving patient outcomes by combining phytochemicals with traditional medicines is examined in this review.

METHODS

The study examined the neuroprotective qualities of ginsenosides, ginkgolides, resveratrol, and curcumin as well as their antioxidant and anti-inflammatory activities. Analysis was done on molecular pathways and medication delivery techniques to improve translational outcomes and drug availability for clinical practice.

RESULTS

Phytochemical substances directly influence TBI-related neurogenic pathways and functional restoration while also affecting subsequent neural damage processes. Particle-based medicine delivery platforms enhance therapeutic drug efficacy, emerging as innovative solutions for targeted drug delivery. When traditional medical therapies integrate with phytochemicals, it becomes possible to achieve better patient results through enhanced synergy.

CONCLUSION

This review uniquely integrates phytochemicals with standard TBI treatments, emphasizing advanced drug delivery strategies and their translational potential to enhance neuroprotection and clinical outcomes. Unlike previous studies, it explores novel drug delivery platforms, such as nanoparticle-based systems, and highlights the synergy between phytochemicals and conventional therapies to improve patient recovery.

Artificial intelligence in diabetes management: transformative potential, challenges, and opportunities in healthcare

BACKGROUND

Diabetes, a chronic metabolic disorder characterized by ineffective blood sugar regulation, affects millions of people worldwide, with its prevalence projected to more than double in the next 30 years. Diabetes-related complications are severe and sometimes life-threatening, including cardiovascular disease, kidney failure, and blindness, this posing a significant challenge, especially in low- and middle-income countries. This study explored the integration of artificial intelligence (AI) into diabetes management, emphasizing its transformative potential in healthcare.

OBJECTIVES

To evaluate the role of AI in enhancing diabetes management and to identify the challenges and opportunities associated with its implementation.

METHODS

A systematic review following the PRISMA guidelines was conducted by analyzing the literature published from January 2020 to May 2024. This review focused on the application of AI in diabetes diagnosis, personalization of treatment, and predictive analytics.

RESULTS

The ability of AI to analyze large datasets and identify complex patterns shows promise in improving diabetes management. AI-assisted diagnostic tools enhance diagnostic accuracy, enable early detection, and support personalized treatment plans, thereby reducing human error. AI has also facilitated research breakthroughs in genomics and drug discovery. Furthermore, AI-powered predictive analytics enhances clinical decision-making and supports precision medicine. Despite these advancements, challenges remain in such issues as data quality, technical infrastructure, and ethical considerations, emphasizing the need for responsible AI development that focuses on patient privacy and transparency.

CONCLUSIONS

AI has significant potential to revolutionize diabetes management and healthcare delivery. Combining AI's analytical processes with clinical expertise can substantially improve the quality of care. Addressing data, technology, and ethical challenges is crucial for fully harnessing AI's potential, thereby enhancing patient well-being and healthcare outcomes.

Lung Cancer: New Directions in Senior Patients Assessment

Age is but one significant prognostic factor in lung cancer, influencing survival, treatment response, and outcomes. This narrative review synthesizes findings from searches of 11 leading databases of research studies, systematic reviews, book chapters, and clinical trial reports on lung cancer in senior patients, with a focus on geriatric assessment as well as biomarkers. Key prognostic factors for lung cancer in seniors include biological age, functional capability, physical and psychological comorbidities, frailty, nutrition, status, and biomarkers like DNA methylation age. We identified the most valuable assessments that balance efficacy with quality of life. Optimizing care and improving outcomes with senior lung cancer patients benefits from a tailored therapeutic approach incorporating a complex geriatric assessment. A multidisciplinary collaboration between geriatricians, oncologists, and pulmonologists is crucial.

AWOT and CWOT for genotype and genotype-by-treatment interaction joint analysis in pharmacogenetics GWAS

MOTIVATION

Pharmacogenomics (PGx) research holds the promise for detecting association between genetic variants and drug responses in randomized clinical trials, but it is limited by small populations and thus has low power to detect signals. It is critical to increase the power of PGx genome-wide association studies (GWAS) with small sample sizes so that variant-drug-response association discoveries are not limited to common variants with extremely large effect.

RESULTS

In this article, we first discuss the challenges of PGx GWAS studies and then propose the adaptively weighted joint test (AWOT) and Cauchy Weighted jOint Test (CWOT), which are two flexible and robust joint tests of the single nucleotide polymorphism main effect and genotype-by-treatment interaction effect for continuous and binary endpoints. Two analytic procedures are proposed to accurately calculate the joint test P-value. We evaluate AWOT and CWOT through extensive simulations under various scenarios. The results show that the proposed AWOT and CWOT control type I error well and outperform existing methods in detecting the most interesting signal patterns in PGx settings (i.e. with strong genotype-by-treatment interaction effects, but weak genotype main effects). We demonstrate the value of AWOT and CWOT by applying them to the PGx GWAS from the Bezlotoxumab Clostridium difficile MODIFY I/II Phase 3 trials.

AVAILABILITY AND IMPLEMENTATION

The R package COWT is publicly available on CRAN https://cran.r-project.org/web/packages/cwot/index.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Pharmacogenomics polygenic risk score for drug response prediction using PRS-PGx methods

Polygenic risk scores (PRS) have been successfully developed for the prediction of human diseases and complex traits in the past years. For drug response prediction in randomized clinical trials, a common practice is to apply PRS built from a disease genome-wide association study (GWAS) directly to a corresponding pharmacogenomics (PGx) setting. Here, we show that such an approach relies on stringent assumptions about the prognostic and predictive effects of the selected genetic variants. We propose a shift from disease PRS to PGx PRS approaches by simultaneously modeling both the prognostic and predictive effects and further make this shift possible by developing a series of PRS-PGx methods, including a novel Bayesian regression approach (PRS-PGx-Bayes). Simulation studies show that PRS-PGx methods generally outperform the disease PRS methods and PRS-PGx-Bayes is superior to all other PRS-PGx methods. We further apply the PRS-PGx methods to PGx GWAS data from a large cardiovascular randomized clinical trial (IMPROVE-IT) to predict treatment related LDL cholesterol reduction. The results demonstrate substantial improvement of PRS-PGx-Bayes in both prediction accuracy and the capability of capturing the treatment-specific predictive effects while compared with the disease PRS approaches.

To try to predict an individual’s drug response using genetic data, most studies have used traditional polygenic risk score (PRS) methods. Here, the authors develop a pharmacogenomics-specific PRS method, which can improve drug response prediction and patient stratification in pharmacogenomics studies.

A statistical perspective on baseline adjustment in pharmacogenomic genome-wide association studies of quantitative change

In pharmacogenetic (PGx) studies, drug response phenotypes are often measured in the form of change in a quantitative trait before and after treatment. There is some debate in recent literature regarding baseline adjustment, or inclusion of pre-treatment or baseline value as a covariate, in PGx genome-wide association studies (GWAS) analysis. Here, we provide a clear statistical perspective on this baseline adjustment issue by running extensive simulations based on nine statistical models to evaluate the influence of baseline adjustment on type I error and power. We then apply these nine models to analyzing the change in low-density lipoprotein cholesterol (LDL-C) levels with ezetimibe + simvastatin combination therapy compared with simvastatin monotherapy therapy in the 5661 participants of the IMPROVE-IT (IMProved Reduction of Outcomes: Vytroin Efficacy International Trial) PGx GWAS, supporting the conclusions drawn from our simulations. Both simulations and GWAS analyses consistently show that baseline-unadjusted models inflate type I error for the variants associated with baseline value if the baseline value is also associated with change from baseline (e.g., when baseline value is a mediator between a variant and change from baseline), while baseline-adjusted models can control type I error in various scenarios. We thus recommend performing baseline-adjusted analyses in PGx GWASs of quantitative change.

A systematic review of miRNAs as biomarkers in osteoporosis disease

Background

Osteoporosis is often considered to be a disease of the elderly, which is characterized by two characteristics: low bone mineral density (BMD) and increased risk of fracture. MicroRNAs (miRNAs) have been reported to play a potential role in bone formation and resorption, bone remodeling, bone homeostasis regulation, and bone cell differentiation. Therefore, altered expression of different miRNAs may impact the pathology of bone diseases such as osteoporosis. A systematic review was conducted to extract all miRNA found to be significantly dys-regulated in the peripheral blood.

Methods

This review was carried out using a systematically search on PubMed, Scopus, Embase, Web of Science (WoS), and Cochrane databases from 1990 to 2018 to explore the diagnostic value of miRNAs as a biomarker in osteoporosis.

Results

A total of 31 studies were identified in the systematic review that indicated more than 30 kinds of up-regulated and down-regulated miRNAs in three categories; postmenopausal osteoporosis, postmenopausal osteoporosis with fracture risk, and other types of osteoporosis and fracture risk.

Conclusion

The collective data presented in this review indicate that miRNAs could serve as biomarkers for the diagnosis (onset) and prognosis (progression of osteoporosis), while the clinical application of these findings has yet to be verified.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-021-00873-5.

Composite Kernel Association Test (CKAT) for SNP-set joint assessment of genotype and genotype-by-treatment interaction in Pharmacogenetics studies

MOTIVATION

It is of substantial interest to discover novel genetic markers that influence drug response in order to develop personalized treatment strategies that maximize therapeutic efficacy and safety. To help enable such discoveries, we focus on testing the association between the cumulative effect of multiple single nucleotide polymorphisms (SNPs) in a particular genomic region and a drug response of interest. However, the currently existing methods are either computational inefficient or not able to control type I error and provide decent power for whole exome or genome analysis in Pharmacogenetics (PGx) studies with small sample sizes.

RESULTS

In this article, we propose the Composite Kernel Association Test (CKAT), a flexible and robust kernel machine-based approach to jointly test the genetic main effect and SNP-treatment interaction effect for SNP-sets in Pharmacogenetics (PGx) assessments embedded within randomized clinical trials. An analytic procedure is developed to accurately calculate the P-value so that computationally extensive procedures (e.g. permutation or perturbation) can be avoided. We evaluate CKAT through extensive simulation studies and application to the gene-level association test of the reduction in Clostridium difficile infection recurrence in patients treated with bezlotoxumab. The results demonstrate that the proposed CKAT controls type I error well for PGx studies, is efficient for whole exome/genome association analysis and provides better power performance than existing methods across multiple scenarios.

AVAILABILITY AND IMPLEMENTATION

The R package CKAT is publicly available on CRAN https://cran.r-project.org/web/packages/CKAT/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Precision Medicine Tools to Guide Therapy and Monitor Response to Treatment in a HER-2+ Gastric Cancer Patient: Case Report

Trastuzumab, has played a major role in improving treatment outcomes in HER-2 positive gastric cancer. However, once there is disease progression there is a paucity of evidence for second line therapy. Patient-derived xenografts (PDXs) in combination with liquid biopsies can help guide individual therapeutic decisions and have now started to be studied. In the present case we established a PDX model from a metastatic HER-2+ gastric cancer patient and after the first engraftment passage we performed a mouse clinical trial to test T-DM1 as an alternative therapy for the patient. The PDX tumor response served as a guide to administer T-DM1 therapy to the patient who responded to treatment before relapsing 6 months later. Throughout out the clinical follow up of the patient, ctDNA levels of HER-2 copy number and a PIK3CA mutation were monitored and we found their correlation with drug response and disease progression to outperform that of CEA levels. This study highlights the utility of applying precision medicine tools combining PDX models to guide therapy with circulating tumor DNA (ctDNA) to monitor treatment response and disease progression.

Genetic structure of pharmacogenetic biomarkers in Brazil inferred from a systematic review and population-based cohorts: a RIBEF/EPIGEN-Brazil initiative

We present allele frequencies involving 39 pharmacogenetic biomarkers studied in Brazil, and their distribution on self-reported race/color categories that: (1) involve a mix of perceptions about ancestry, morphological traits, and cultural/identity issues, being social constructs pervasively used in Brazilian society and medical studies; (2) are associated with disparities in access to health services, as well as in their representation in genetic studies, and (3), as we report here, explain a larger portion of the variance of pharmaco-allele frequencies than geography. We integrated a systematic review of studies on healthy volunteers (years 1968-2017) and the analysis of allele frequencies on three population-based cohorts from northeast, southeast, and south, the most populated regions of Brazil. Cross-validation of results from these both approaches suggest that, despite methodological heterogeneity of the 120 studies conducted on 51,747 healthy volunteers, allele frequencies estimates from systematic review are reliable. We report differences in allele frequencies between color categories that persist despite the homogenizing effect of >500 years of admixture. Among clinically relevant variants: CYP2C9*2 (null), CYP3A5*3 (defective), SLCO1B1-rs4149056(C), and VKORC1-rs9923231(A) are more frequent in Whites than in Blacks. Brazilian Native Americans show lower frequencies of CYP2C9*2, CYP2C19*17 (increased activity), and higher of SLCO1B1-rs4149056(C) than other Brazilian populations. We present the most current and informative database of pharmaco-allele frequencies in Brazilian healthy volunteers.

Biomarkers and personalized medicine: current status and further perspectives with special focus on dermatology

Biomarkers are of increasingly high importance in medicine, particularly in the realm of 'personalized medicine'. They are valuable for predicting prognosis and dose selection. Moreover, they may be helpful in detecting therapeutic and adverse responses and in patient stratification based on efficacy or safety prediction. Thus, biomarkers are essential tools for the selection of appropriate patients for treatment with certain drugs to and enable personalized medicine, that is 'providing the right treatment to the right patient, at the right dose at the right time'. Currently, there are six drugs approved for dermatological indications with recommended or mandatory biomarker testing. Most of them are used to treat melanoma and human immunodeficiency virus infection. In contrast to the few fully validated biomarkers, many exploratory biomarkers and biomarker candidates have potential applications. Prognostic biomarkers are of particular significance for malignant conditions. Similarly, diagnostic biomarkers are important in autoimmune diseases. Disease severity biomarkers are helpful tools in the treatment for inflammatory skin diseases. Identification, qualification and implementation of the different kinds of biomarkers are challenging and frequently necessitate collaborative efforts. This is particularly true for stratification biomarkers that require a companion diagnostic marker that is co-developed with a certain drug. In this article general definitions and requirements for biomarkers as well as for the impact of biomarkers in dermatology are reviewed and opportunities and challenges are discussed.

CYP2D6 genotype is not associated with survival in breast cancer patients treated with tamoxifen: results from a population-based study

PURPOSE

A number of studies have tested the hypothesis that breast cancer patients with low-activity CYP2D6 genotypes achieve inferior benefit from tamoxifen treatment, putatively due to lack of metabolic activation to endoxifen. Studies have provided conflicting data, and meta-analyses suggest a small but significant increase in cancer recurrence, necessitating additional studies to allow for accurate effect assessment. We conducted a retrospective pharmacogenomic analysis of a prospectively collected community-based cohort of patients with estrogen receptor-positive breast cancer to test for associations between low-activity CYP2D6 genotype and disease outcome in 500 patients treated with adjuvant tamoxifen monotherapy and 500 who did not receive any systemic adjuvant therapy.

METHODS

Tumor-derived DNA was genotyped for common, functionally consequential CYP2D6 polymorphisms (*2, *3, *4, *6, *10, *41, and copy number variants) and assigned a CYP2D6 activity score (AS) ranging from none (0) to full (2). Patients with poor metabolizer (AS = 0) phenotype were compared to patients with AS > 0 and in secondary analyses AS was analyzed quantitatively. Clinical outcome of interest was recurrence free survival (RFS) and analyses using long-rank test were adjusted for relevant clinical covariates (nodal status, tumor size, etc.).

RESULTS

CYP2D6 AS was not associated with RFS in tamoxifen treated patients in univariate analyses (p > 0.2). In adjusted analyses, increasing AS was associated with inferior RFS (Hazard ratio 1.43, 95% confidence interval 1.00-2.04, p = 0.05). In patients that did not receive tamoxifen treatment, increasing CYP2D6 AS, and AS > 0, were associated with superior RFS (each p = 0.0015).

CONCLUSIONS

This population-based study does not support the hypothesis that patients with diminished CYP2D6 activity achieve inferior tamoxifen benefit. These contradictory findings suggest that the association between CYP2D6 genotype and tamoxifen treatment efficacy is null or near null, and unlikely to be useful in clinical practice.

Identification of drug-induced toxicity biomarkers for treatment determination

Drug-induced organ toxicity (DIOT) that leads to the removal of marketed drugs or termination of candidate drugs has been a leading concern for regulatory agencies and pharmaceutical companies. In safety studies, the genomic assays are conducted after the treatment so that drug-induced adverse effects can occur. Two types of biomarkers are observed: biomarkers of susceptibility and biomarkers of response. This paper presents a statistical model to distinguish two types of biomarkers and procedures to identify susceptible subpopulations. The biomarkers identified are used to develop classification model to identify susceptible subpopulation. Two methods to identify susceptibility biomarkers were evaluated in terms of predictive performance in subpopulation identification, including sensitivity, specificity, and accuracy. Method 1 considered the traditional linear model with a variable-by-treatment interaction term, and Method 2 considered fitting a single predictor variable model using only treatment data. Monte Carlo simulation studies were conducted to evaluate the performance of the two methods and impact of the subpopulation prevalence, probability of DIOT, and sample size on the predictive performance. Method 2 appeared to outperform Method 1, which was due to the lack of power for testing the interaction effect. Important statistical issues and challenges regarding identification of preclinical DIOT biomarkers were discussed. In summary, identification of predictive biomarkers for treatment determination highly depends on the subpopulation prevalence. When the proportion of susceptible subpopulation is 1% or less, a very large sample size is needed to ensure observing sufficient number of DIOT responses for biomarker and/or subpopulation identifications.

Diabetic retinopathy: variations in patient therapeutic outcomes and pharmacogenomics

Diabetes and its microvascular complications in patients poses a significant challenge and constitutes a major health problem. When it comes to manifestations in the eye, each case of diabetic retinopathy (DR) is unique, in terms of the phenotype, genotype, and, more importantly, the therapeutic response. It is therefore important to identify factors that distinguish one patient from another. Personalized therapy in DR is a new trend aimed at achieving maximum therapeutic response in patients by identifying genotypic and phenotypic factors that may result in less than optimal response to conventional therapy, and consequently, lead to poorer outcome. With advances in the identification of these genetic markers, such as gene polymorphisms and human leucocyte antigen associations, as well as development of drugs that can target their effects, the future of personalized medicine in DR is promising. In this comprehensive review, data from various studies have been analyzed to present what has been achieved in the field of pharmacogenomics thus far. An insight into future research is also provided.

A composite model for subgroup identification and prediction via bicluster analysis

Background

A major challenges in the analysis of large and complex biomedical data is to develop an approach for 1) identifying distinct subgroups in the sampled populations, 2) characterizing their relationships among subgroups, and 3) developing a prediction model to classify subgroup memberships of new samples by finding a set of predictors. Each subgroup can represent different pathogen serotypes of microorganisms, different tumor subtypes in cancer patients, or different genetic makeups of patients related to treatment response.

Methods

This paper proposes a composite model for subgroup identification and prediction using biclusters. A biclustering technique is first used to identify a set of biclusters from the sampled data. For each bicluster, a subgroup-specific binary classifier is built to determine if a particular sample is either inside or outside the bicluster. A composite model, which consists of all binary classifiers, is constructed to classify samples into several disjoint subgroups. The proposed composite model neither depends on any specific biclustering algorithm or patterns of biclusters, nor on any classification algorithms.

Results

The composite model was shown to have an overall accuracy of 97.4% for a synthetic dataset consisting of four subgroups. The model was applied to two datasets where the sample’s subgroup memberships were known. The procedure showed 83.7% accuracy in discriminating lung cancer adenocarcinoma and squamous carcinoma subtypes, and was able to identify 5 serotypes and several subtypes with about 94% accuracy in a pathogen dataset.

Conclusion

The composite model presents a novel approach to developing a biclustering-based classification model from unlabeled sampled data. The proposed approach combines unsupervised biclustering and supervised classification techniques to classify samples into disjoint subgroups based on their associated attributes, such as genotypic factors, phenotypic outcomes, efficacy/safety measures, or responses to treatments. The procedure is useful for identification of unknown species or new biomarkers for targeted therapy.