Browsing by Subject "Pharmacogenomics"
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Item Application of pharmacometrics for covariate selection and dose optimization of tacrolimus in adult kidney transplant recipients(2012-12) Passey, ChaitaliIn spite of rigorous dose adjustments by way of therapeutic drug monitoring, a large proportion of kidney transplant recipients are unable to achieve the target tacrolimus trough concentrations. This is attributed to the narrow therapeutic window of the drug (10-15 ng/mL) and large inter-individual variability in pharmacokinetic parameter such as clearance. There is a need for development of clinical dosing models that can help prospectively predict the dose for an individual, especially in the critical period immediately post-transplant. Therefore, we established and quantified the effect of clinical and genetic factors on tacrolimus clearance (CL/F) using a large population of adult kidney transplant recipients. Tacrolimus troughs (n=11823) from 681 transplant recipients over the first 6-months post-transplant were analyzed using non-linear mixed effects modeling approach in NONMEM®. The troughs were characterized by a steady state infusion model. Covariates were analyzed using a forward selection (p<0.0.1) backward elimination (p<0.001) approach. We formulated an equation that predicts the CL/F of an individual based on the days post-transplant, presence of the highly influential CYP3A5*1 genotype, transplant at a steroid sparing center, age and concomitant use of a calcium channel blocker at the time of trough collection. The CL/F was seen to decrease with increasing days post transplant, transplant at a steroid sparing center and use of a calcium channel blocker. Transplant recipients with the CYP3A5*1/*3 and *1/*1 genotypes had a CL/F that was 70% and 100% higher, respectively, than those with the CYP3A5*3/*3 genotype. The dose required in order to achieve a particular target trough can be prospectively determined from this equation. The above equation was validated in a separate cohort of adult kidney transplant recipients. The equation was assessed by predictive performance in 795 transplant recipients (n=13,968 troughs) receiving tacrolimus using bias and precision. Assessment was done for the initial troughs as well as for all troughs over the entire 6 months. The equation has low bias (0.2 ng/ml) and good precision (within ± 20% for a typical trough of 10 ng/mL) in predicting initial troughs and could be safely used to predict initial doses. This is critical as an accurate initial dose will help the recipient to get to therapeutic range faster and reduce the number of out-of-range troughs. For all the troughs, over the 6 months post-transplant, the equation did better than a basic model with no covariates but had higher bias and imprecision than the prediction of initial troughs. We were presented with 119 single nucleotide polymorphisms (SNP) in this study. Due to software limitations and impracticalities associated with such a large number of covariates, we developed and validated a novel "winnowing method" of covariate selection that is able to test and select SNPs in combination. This method uses random selection, repetitions of generalized additive modeling in the R statistical package and post-hoc estimates from NONMEM®. The salient feature of this method is the creation of an index, ranging from 0-1, that defines the relative importance of the SNP when tested in a combination. With this method, we were able to select 26 SNPs out of the 119 SNPs, which included the well-established CYP3A5*1 SNP. We validated this method using a simulated dataset. In the validation dataset, the winnowing method was able to select all the important SNPs. The type I and type II error rates were 9% and 0% respectively. Although NONMEM® is the oldest and most widely used population pharmacokinetics software, several other software packages are now becoming available such as the Phoenix® NLMETM. One desirable feature in this new software package is a graphical user interface and menu-driven covariate selection options. Therefore, we compared these two software packages in terms of covariates selected and predictive performance using both clinical and simulated data. For the tacrolimus data, NONMEM® predictions had lower bias and imprecision as compared to Phoenix® NLMETM. For the clinical data, NONMEM® predictions had higher bias but were more precise than the Phoenix® NLMETM predictions.>Item The Development And Application Of Machine Learning For Drug Discovery And Drug Response Prediction For Personalized Cancer Treatment(2024) Stover, DanielleIn the field of pharmacogenomics and precision medicine, gene expression analysis has become a crucial tool in predicting patient drug response. My contributions to this field come primarily in the development and application of two bioinformatic packages: oncoPredict and scIDUC. oncoPredict is a tool based in the R programming language, primarily used to predict the response of various cancer samples (cell line, patient, etc.) to different drugs. This is made possible by incorporating machine learning to analyze the complex relationships between genomic features and drug response from pan-cancer cell lines. These relationships are learned from microarray or bulk RNA sequencing (RNA-seq) data and high-throughput drug screens, then applied to patient data to generate novel drug discovery hypotheses. In turn, oncoPredict aids in identifying potential drug candidates, understanding mechanisms of drug resistance, and predicting the effectiveness of drugs on specific cancer types. scIDUC (single-cell Integration and Drug Utility Computation) is a computational framework based in python that quickly and accurately generates predictions of drug response for cells derived from single-cell RNA sequencing (scRNA-seq) data. It is a transfer learning-based approach that learns relationships between drug sensitivities and relevant gene expression patterns based on cell line bulk RNA-seq data and high-throughput drug screens, similar to oncoPredict. The key difference, however, is that prior to training drug response models, scIDUC integrates bulk RNA-seq and target scRNA-seq data to denoise and extract shared gene expression patterns between bulk and single-cell data sources. The resulting bulk data is then used to train drug response models, whose coefficients are further applied to post-integration single-cell data to infer cellular drug sensitivity scores.Item Improving hematopoietic cell transplantation therapeutics:emphasis in pharmacokinetic-pharmacodynamic relationships and pharmacogenomics.(2009-12) Long-Boyle, Janel ReneeTreatment-related mortality and acute graft vs host disease remain prominent clinical problems in nonmyeloablative allogeneic hematopoietic cell transplantation (HCT). Hence, the need for improved preparative regimens and immunosuppressive strategies in HCT persists. The research presented in my dissertation will be focused on defining pharmacokinetic-pharmacodynamic relationships, and pharmacogenomics involving two antineoplastic agents, fludarabine and clofarabine, and the immunosuppressive agent, mycophenolate all of which are used in the setting of HCT. Fludarabine is a purine analog commonly used in both adult and pediatric nonmyeloablative allogeneic HCT. Although the pharmacokinetics of fludarabine have been extensively studied in a variety of malignant diseases, very little data is available in nonmyeloablative HCT and the relationship between fludarabine pharmacokinetic parameters and clinical outcomes such as treatment-related mortality have yet to be evaluated. Similarly, no PK data is available for clofarabine; a newer purine analog currently used pediatric patients undergoing HCT for non-hematologic malignancies. Finally, mycophenolic acid pharmacokinetics in HCT recipients displays wide inter- and intra-patient variability in plasma concentrations and low mycophenolate exposure is associated with lower rates of engraftment and greater risk of acute graft vs host disease. Patient characteristics such as weight or body surface area, or clinical markers for hepatic and renal function incompletely explain pharmacokinetic variability suggesting there may be genetic factors influencing mycophenolate metabolism or transport. The methodologies and techniques employed to evaluate each individual agent will differ, including pharmacokinetic and statistical analyses. However, all projects share the common goal of improving patient outcomes and reducing toxicity in this very complex patient population.Item MicroRNAs As Predictors of Nucleoside Analog Sensitivity in Acute Myeloid Leukemia(2015-12) Bhise, NehaAcute myeloid leukemia (AML) is the most aggressive form of hematological malignancies. Despite advances in treating AML, development of resistance to nucleoside analog therapy remains one of the major obstacles in AML treatment. Various factors, including SNPs in genes, epigenetics, etc. play a role in mediating variability in response to nucleoside analog therapy. Recent studies have shown that microRNAs can also serve as regulators of gene expression that can contribute to variability in response to therapeutic agents. Thus the main objective of the thesis was to determine the role of microRNAs as predictors of variability in nucleoside analog response. To our knowledge no studies have been reported that identify microRNAs as predictors of response to cytarabine therapy in AML patients. In chapter II we used a translational approach of conducting in vitro and clinical study to identify microRNAs that were predictive of overall survival in AML patients. Our study conclusively identified that miR107-Myb, miR-378-granzyme B and miR10a-MAP4K4 as miRNA-mRNA pairs that can be used as predictors of overall survival in AML patients. Additionally, we also showed that the miRNAs mechanistically regulate the expression of these mRNAs by binding to the 3’- untranslated region of these mRNAs. miRNAs can also cause variability in response to cytarabine therapy by regulating the expression of the genes involved in disposition of cytarabine. In chapter III we identified that miR-24 and miR-34a as regulator of DCTD (an enzyme involved in inactivation of cytarabine) and DCK (activating enzyme), respectively. These miRNAs along with other miRNAs can be used as part of biomarker signature that can be used to predict the overall survival in AML patients. In chapter IV, we determined the impact of cytarabine treatment on in vivo cytarabine-induced changes in leukemia cell transcriptome and miRNA expression, to evaluate their impact on clinical outcome. In the first part of this chapter, we identified key genes (such as tumor suppressors DKK3, TRIM33, PBRM1, an oncogene SET, cytidine-deaminase family members APOBEC2 and APOBEC3G) influenced by cytarabine infusion that were also predictive of response. In the second part of this chapter, using data from clinical studies, we identified several miRNAs that were altered by cytarabine treatment. The changes in the expression of these miRNAs resulted in alteration in gene expression that correlated with the overall survival in AML patients. Additionally, using cell lines we were able to identify various miRNA – mRNAs that were altered by drug treatment, indicating that the therapy itself can influence the predictive ability of miRNAs as biomarkers. Significant data has been published on cytarabine as it is the standard of care in AML patients, however, there is limited knowledge about newer nucleoside analogs such as clofarabine. In chapter V, using in vitro methods, we identified several microRNAs, such as miR-16, miR-515 cluster, etc., that can be used as predictors of response for clofarabine therapy. Our data clearly suggests that there are several distinct microRNAs that can be used as predictors of response to clofarabine therapy in AML patients. We propose doing a clinical study to evaluate the clinical utility of these miRNAs as predictors of response. In summary, using a translational approach, we were able to identify miRNAs and several miRNA-mRNA pairs that can be used as biomarkers of response to cytarabine therapy. Additionally, we also identified that drug therapy itself can influence the outcome in AML patients. These findings are clinically important as they will help provide a new strategy to optimize dosing of nucleoside analogs in AML patients which in turn would lead to better overall survival while reducing the side effects.Item Personalizing Colorectal Cancer Care: Opportunities for Pharmacogenomics(2021-09) Rivers, ZacharyA diagnosis of metastatic colorectal cancer impacts nearly 30,000 Americans a year, and while treatments are available, they come with the risk of morbidity and mortality. Personalized medicine, the use of an individual’s genetic information to tailor their treatment, is used in oncology to determine somatic, druggable targets to select therapy. It also provides an opportunity to understand the likelihood that an individual would develop toxicities during treatment or fail to respond to treatment. This approach has not been integrated into clinical practice at the same level as the targeted approach. This dissertation explores the opportunities for germline pharmacogenetic testing to inform chemotherapy and non-chemotherapy medication selection in a historical cohort of Americans with metastatic colorectal cancer and models the theoretical cost-effectiveness of implementing this approach.Item Personalizing Therapy In Transplantation: Focus On Pharmacokinetics, Pharmacodynamics And Pharmacogenomics Of Drugs Used In Hematopoeitic Stem Cell And Kidney Transplant(2016-05) Sanghavi, KinjalPatients treated with a standardized dosing strategy often demonstrate a substantial variability in drug response. Number of factors influences systemic exposure of the drug and its effect on the biological targets. The central objective of this thesis was to identify biomarkers and develop personalized dosing of drugs used in hematopoietic stem cell transplant (HSCT) and kidney transplant to improve outcomes. Fludarabine is a chemotherapeutic drug used in reduced intensity conditioning (RIC) HSCT. High fludarabine exposure is associated with greater treatment related mortality (TRM). Fludarabine dose reductions are commonly empirical for obese and/or those with renal dysfunction. We developed a dosing equation, accounting for creatinine clearance and body size. Using this model to make dose reductions will reduce the probability of fludarabine overexposure and reduce TRM. Cyclophosphamide (Cy) is another chemotherapeutic agent used in RIC HSCT, associated with high toxicity and TRM. Due to complex metabolic pathway it is unclear which metabolite is most important to predict Cy’s efficacy and toxicity. We evaluated the association between the active metabolite, phosphoramide mustard (PM), exposure and TRM. We found that higher PM AUC of was associated with greater TRM. We further identified creatinine clearance and gender to influence PM clearance and volume of distribution respectively. Tacrolimus is an immunosuppressant used in kidney transplant recipients. African Americans show very high variability in tacrolimus exposure and poor outcomes. We developed a tacrolimus dosing model, taking into account the clinical and genetic variants to individualize dose in African Americans that could help achieve the target concentrations quicker and improve outcomes. Mycophenolic acid (MPA) is another immunosuppressant used in kidney transplant recipients. Enterohepatic recycling and high variability in trough concentrations make it very difficult to use MPA concentrations for routine therapeutic monitoring. We conducted an RNA sequencing analysis to measure gene expression to identify novel biomarkers to predict MPA efficacy and toxicity. We identified transient changes in gene expression post MPA administration and that expression of 3 genes out of ~20000 were significantly associated with MPA trough concentrations. Additional studies are required to identify if transient changes in gene expression are associated with MPA related outcomes.Item Pharmacogenetic Investigations Using Community-Based Participatory Research to Address Health Disparities in Minnesota Hmong(2017-08) Roman, YoussefIntroduction: Pharmacogenomics is an approach to personalizing therapy to help patients achieve their therapeutic goals with the least possible adverse events. This approach relies on the knowledge derived from large genetic studies that involve diverse populations to guide the development of treatment algorithms. The underrepresentation of select populations or unique sub-populations in genetic-based research presents as a gap in knowledge to create comprehensive genetic-based treatment algorithms and a missed opportunity to address health disparities within those unique populations. A prime example is the Minnesota Hmong. The Hmong is an Asian sub-population minimally represented in clinical or genetic-based research with a high prevalence of gout and gout-related comorbidities than non-Hmong. Methods: Using the principles of community-based participatory research and the establishment of the Hmong advisory board, assessment of the community’s perception of genetics and preparedness for engagement in research were conducted. Capitalizing on the findings from the first informational study, two Hmong genetic-based studies were conducted. The first study was to ascertain the frequency of select pharmacogenes and disease-risk genes in the Hmong, relative to non-Hmong. The second study was to quantify the effect of genetic variations within uric acid transportome and purine metabolizing genes on the pharmacokinetics and pharmacodynamics of allopurinol in Hmong adults with gout or hyperuricemia. Results: The informational study results indicated that most Hmong are willing to participate in research to help themselves and the Hmong community. Some of the genetic perceptions in the Hmong were not scientifically grounded and some concerns about privacy were reported while the return of genetic results to participants had mixed responses. The first genetic-based study indicated that more than 80% of Hmong participants were willing to store their DNA for future analyses and share their DNA with other scientists. Pharmacogenes risk allele frequencies of CYP2C19, CYP2C9, VKORC1, and CYP4F2 were higher in the Hmong relative to Caucasian. Disease risk allele frequencies of hyperuricemia and gout associated genes such as SLC2A9, SLC17A1, SLC22A11, SLC22A12, ABCG2, PDZK1, were also higher in the Hmong than Caucasian and Han-Chinese. The second genetic-based study indicated that the genetic variation within SLC22A12 (rs505803T>C) significantly affects the exposure to and the renal clearance of the active metabolite of allopurinol, oxipurinol. Additionally, the rs505802 was also significantly associated with the overall response to allopurinol. Conclusions: Engaging the Hmong in genetic-based research is a step forward to advance precision medicine while addressing health disparities within the Hmong community. The prevalence of pharmacogenes within the Hmong suggest that the Hmong will require a lower starting dose of warfarin and unlikely to benefit from clopidogrel. The prevalence of hyperuricemia and gout associated risk alleles in the Hmong are consistent with the higher prevalence of gout in the Hmong. Finally, the rs505802 T>C within SLC22A12 gene could predict the overall response to allopurinol.Item Pharmacogenomics with a Prospective Trial Looking at How Pharmacogenomic Assessment Might Impact Blood and Marrow Transplant Patients(2023-10) Thoma, MaryPharmacogenomics (PGx) is an evolving branch of medicine in Individualized Medicine that assesses potential response to medications by correlating these with a person’s genetic profile. PGx studies assess genes responsible for the coding of the proteins associated with drug metabolism. These studies link predicted phenotypes to the patient-specific haplotypes. There are databases housing the information nationally and internationally, and companies providing resources to assess PGx profiles for people and give recommendations for actions on these. There are obstacles, however, preventing widespread adoption of this science into mainstream medicine. We recognize the potential of the data to impact patient care but have yet to implement the systems needed to accrue and analyze data on a broad population scale to identify the many genetic factors involved. We recognize many interindividual differences but have yet to fully recognize the interplay of factors including ethnic variables, histone modification, and associated mutations that can offset specific PGx mutations. We lack a universally functional patient electronic medical record that can interface with databases accruing PGx data to provide reliable and translatable data for providers and patients. Assessing PGx patient profiles is now relatively fast and inexpensive, but still lacks the wide-spread applicability in medical practice that it could have. This thesis will initially review the current state of PGx research. It looks at some of the methods and design approaches to PGx research. It then will delve into how this research is assimilated, stored, and made accessible to the companies and providers using this today. It will discuss some of the limitations influencing predictive outcomes in the research being done. This thesis then looks at a particular subset of patients; Blood and Marrow Transplant (BMT) patients. These patients are at high risk for having a deleterious outcome from medication reactions, as they are started on multiple medications at a single point in time. This thesis will review possible PGx pathways that would lend to mutational targets for assessment in these patients. This thesis discusses data that suggests metabolizer subtypes that may occur with these mutations. This thesis also discusses how subtherapeutic or supratherapeutic drug levels can occur at standard dosing based on mutations in enzymatic pathways. It explores the literature linking these PGx targets to drugs used in transplant. This will lead into why we included the PGx targets in the prospective trial we undertook for BMT patients. We present the results of a prospective trial we conducted. We obtained PGx data on 50 BMT patients and assessed this for mutational influence on drug metabolism. We present data that we published on the impact of PGx mutations and metabolism of Tacrolimus and cyclosporine by comparing dosing and assessment of drug levels in these patients in the first 100 days after transplant. We assessed possible deleterious effects of Methotrexate by assessing whether patients were able to complete their prescribed course of this drug, without side effects which can limit the use of this medication. We found no statistically significant link to mutational variants and Methotrexate side effect. We discuss these results in more detail. We also discuss further targets that, based on prior PGx studies, were thought to possibly impact the metabolism of medications used in BMT patients. We included these in our original study design, but not in the published results. One mutational target was thought to influence metabolism of Voriconazole and proton pump inhibitors. Two others were thought to influence narcotics or opioids. The results of analysis of these targets were not statistically significant, and we assess and discuss this in detail. We then conclude the analysis of our study results by including a case report with a specific patient impact assessment. This exemplifies why this PGx research is critically needed in real time to help get patients the right drugs at the right doses to meet their needs. It shows how possible PGx variables can contribute to a cascade of deleterious events, even if they aren’t shown to be the inciting event leading to a crisis. We then conclude with a synopsis of the thesis. We include a brief discussion of the research and potential for future directions.Item Precision Medicine Approaches to Immunosuppression Using Pharmacogenomics and the Microbiome(2022-08) Saqr, AbdelrahmanIn this thesis, I provide two examples of precision medicine applications to currently unsolved drug related problems. In chapter 1, the influence of the microbiome on the enterohepatic recirculation of mycophenolate mofetil (MMF) in hematopoietic stem cell transplant recipients (HCT) is described. There is substantial unexplained interindividual variability in MMF pharmacokinetics. This work illustrates that variability in the gut microbiome composition is associated with enterohepatic recirculation of the mycophenolic acid (MPA), the active metabolite of MMF, and consequently differences in drug exposure. In chapter 2, the influence of CYP3A4 and CYP3A5 genotypes on the magnitude of the drug-drug interaction between tacrolimus and steroids in kidney transplant recipients is described. This drug-drug interaction, while well-known, is unpredictable. Some individuals have an induction of tacrolimus clearance in the presence of steroids and others have little to no changes in clearance. This analysis shows that individuals who carry a loss of function allele such as CYP3A5*3 have a minor and clinically insignificant drug-drug interaction whereas individuals who express CYP3A5 and carry at least one CYP3A5*1 allele have a significant tacrolimus-steroid interaction which results in higher tacrolimus dose requirements during steroid use.Item Toward Individualized Medicine in Understudied Populations using Personal Genome and Microbiome(2022-03) Wen, Ya-FengOn January 30, 2015, the US President Barak Obama first announced the Precision Medicine Initiative cohort program in his State of Union address. This program emphasized the critical need for creative approaches to precision medicine and use the evidence to guide clinical practice. Following this announcement, All of Us network was established in July 2016 with a target of enrolling at least 1 million persons with diverse ethnic backgrounds to discover genetic and environmental factors that correlated with disease, to improve predictions of therapeutic safety and efficacy, to discover disease biomarkers, to improve understanding of health disparities, to return data to participants, and many others. These themes with the All of Us initiative resonate with our goals to actively include diverse populations in research programs. This thesis demonstrates research projects which enrolled an understudied population in clinical studies with various objectives, including 1) identify and quantify commonly tested and novel genetic variants within very important pharmacogenes; 2) develop a genotype-guided strategy for allopurinol dose selection in patients with gout to increase attainment of treatment success; and 3) design a clinical study to quantitatively examine how genetics and microbiome can impact the serum-lowering effect of vitamin C. We found significant differences in allele frequencies between the Hmong and East Asians for 23% (5/22) of the CPIC actionable variants tested. These pharmacogenes include CYP2C9*3A, CYP2C19*2, CYP2C19*3, CYP4F2*3, and SLCO1B1*5. Additionally, a higher portion of Hmong participants (50%) are predicted to have an intermediate metabolizer phenotype for CYP2D6 compared to other East Asians which range between 27%-44%. The differences significantly influenced predicted medication usage recommendations in warfarin, simvastatin, and phenytoin, and many other drugs metabolized by CYP2D6 between Hmong and East Asians. Three novel suballeles within CYP2D6 (*10.007, *36.004, and *75.002) were also identified in the Hmong population. Our findings underscore the importance of thoroughly interrogating unique subpopulations to accurately predict a patient’s metabolizer status for key drug metabolizing enzymes and transporters. Combining the knowledge of pharmacogenes and population pharmacokinetics and pharmacodynamics, we proposed an allopurinol dosing guide based on fat-free mass, renal function, and SLC22A12 and PDZK1 genotype to achieve target serum urate, a critical biomarker for gout in the Hmong. This observation is significant due to the high prevalence and disease burden of gout observed in this population. Finally, we designed and conducted a prospective open-labelled clinical trial to quantify the impact of vitamin C on serum urate in Hmong adults with and without hyperuricemia and/or gout, and to identify associations between vitamin C, transporter genomics, gut microbiome, and consequent serum urate level. The development an individualized strategy combining knowledge of pharmacogenes, microbiota, and patients’ characteristics to select optimal therapies that can safely and effectively tailor treatment represents a promising approach to select the right medication with the right dose in the Hmong patients. The research strategies demonstrated in this these are expected to positively impact the severity of illness, mortality, and healthcare costs for Hmong and other understudied populations with many other related diseases.