Pharmacogenomic Models Research Articles

Development of a Pharmacogenomics Model based on Support Vector Regression with Optimal Features Selection Approach to Determine the Initial Therapeutic Dose of Warfarin Anticoagulant Drug

Introduction: Using artificial intelligence tools in pharmacogenomics is one of the latest bioinformatics research fields. One of the most important drugs that determining its initial therapeutic dose is difficult is the anticoagulant warfarin. Warfarin is an oral anticoagulant that, due to its narrow therapeutic window and complex interrelationships of individual factors, the selection of its optimal dose is challenging . Method: Inaccuracy in determining the initial dose of warfarin will simply lead to thrombosis or severe bleeding and ultimately, patient death. Among the relatively successful methods of kernel-based estimation, comparison and identification of suitable kernels have not been researched. In the present research, while carefully examining this approach, different features of selection algorithms were analyzed based on expert opinions, and an appropriate subset of efficient predictor variables was identified for dose estimation. Results: In the current study, a dataset collected by the International Warfarin Consortium was used. The results showed that the support vector machine with a suitable kernel and a subset of the proposed features can successfully predict the ideal dose of warfarin for a significant percentage of patients with an error of approximately 0.7 mg per week . Conclusion: The estimation was conducted using the least squares version of the support vector regression based on a suitable kernel and feature selection strategy. In this way, a better approach for predicting the optimal therapeutic dose of warfarin was presented, which can significantly reduce the wrong dose error and its consequences .

DESIGN AND DEVELOPMENT OF A PHARMACOGENOMIC MODEL FOR BREAST CANCER TO STUDY THE VARIATION IN DRUG ACTION AND SIDE EFFECTS

Objective: The proneness of disease, as well as drug action and side effects, vary from person to person. This may be due to individual variations in the genome. The individual variation demands the need to design a population-specific 'predictive, preventive, participatory and personalized (p4)' pharmacogenomics drug molecule. The present work aims at designing a pharmacogenomic model for breast cancer to explain the individual variation in the proneness of the diseases and susceptibility towards drug action. Methods: The drug action and side effects of drugs were analyzed from clinical trial reports. The genes responsible for the drug action and the genes responsible for side effects have been identified and included in the variation analysis. The pharmacogenomic gene models have been designed by inducing population-specific genetic variations within the gene sequence. The 3D structures of the 'variation-specific' protein models have been generated by 'homology modelling.' These models have been used further for docking studies with the known drug molecules. The kinetic stability of the protein-ligand complexes obtained out of docking studies has been studied by the molecular dynamic simulation. Results: By the interaction studies and the computational analysis using the 'population-specific protein models,' the drug molecule, Capecitabine showed the highest binding affinity (–6.30kcal/mol) with the African population, Paclitaxel was found to be the most interacting with the European population with a binding affinity of–9.5603 kcal/mol, and Lapatinib is found to be the most suitable ligand for the American population with a binding affinity of–6.90 kcal/mol. These observations agree with the clinical trial data found in the 'ClinTrial database'. Conclusion: The designed models are found to be suitable for representing the respective population-specific target models. The interaction studies of known drug molecules with these population-specific target models correspond to the observations in the 'ClinTrial database.'

Extending the lymphoblastoid cell line model for drug combination pharmacogenomics.

Combination drug therapies have become an integral part of precision oncology, and while evidence of clinical effectiveness continues to grow, the underlying mechanisms supporting synergy are poorly understood. Immortalized human lymphoblastoid cell lines (LCLs) have been proven as a particularly useful, scalableand low-cost model in pharmacogenetics research, and are suitable for elucidating the molecular mechanisms of synergistic combination therapies. In this review, we cover the advantages of LCLs in synergy pharmacogenomics and consider recent studies providing initial evidence of the utility of LCLs in synergy research. We also discuss several opportunities for LCL-based systems to address gaps in the research through the expansion of testing regimens, assessment of new drug classes and higher-order combinations, and utilization of integrated omics technologies.

Pharmacokinetic-Pharmacogenomic Model for Chinese Children Treated With Lamotrigine Also Applies for Mexican Children.

Pharmacokinetic-Pharmacogenomic Model for Chinese Children Treated With Lamotrigine Also Applies for Mexican Children.

Predictive model of bosentan-induced liver toxicity in Japanese patients with pulmonary arterial hypertension.

Bosentan, an endothelin receptor antagonist, has been widely used as a first-line medication for the treatment of pulmonary arterial hypertension (PAH). It has been shown to improve symptoms of hypertension, exercise capacity, and hemodynamics and prolong time to clinical worsening. However, liver dysfunction is a major side effect of bosentan treatment that could hamper the optimal management of patients with PAH. Previously, we demonstrated, using drug metabolism enzymes and transporters analysis, that the carbohydrate sulfotransferase 3 (CHST3) and CHST13 alleles are significantly more frequent in patients with elevated aminotransferases during therapy with bosentan than they are in patients without liver toxicity. In addition, we constructed a pharmacogenomics model to predict bosentan-induced liver injury in patients with PAH using two single-nucleotide polymorphisms and two nongenetic factors. The purpose of the present study was to externally validate the predictive model of bosentan-induced liver toxicity in Japanese patients. We evaluated five cases of patients treated with bosentan, and one presented with liver dysfunction. We applied mutation alleles of CHST3 and CHST13, serum creatinine, and age to our model to predict liver dysfunction. The sensitivity and specificity were calculated as 100% and 50%, respectively. Considering that PAH is a rare disease, multicenter collaboration would be necessary to validate our model.

Identifying key transcription factors for pharmacogenetic studies of antipsychotics induced extrapyramidal symptoms

We explore the transcription factors involved in the molecular mechanism of antipsychotic (AP)-induced acute extrapyramidalsymptoms (EPS) in order to identify new candidate genes for pharmacogenetic studies. Protein-protein interaction (PPI) networks previously created from three pharmacogenomic models (in vitro, animal, and peripheral blood inhumans) were used to, by means of several bioinformatic tools; identify key transcription factors (TFs) that regulate each network. Once the TFs wereidentified, SNPs disrupting the binding sites (TFBS) of these TFs in the genes of each network were selected for genotyping. Finally, SNP-basedassociations with EPS were analyzed in a sample of 356 psychiatric patients receiving AP. Our analysis identified 33 TFs expressed in the striatum, and 125 SNPs disrupting TFBS in 50 genes of our initial networks. Two SNPs (rs938112,rs2987902) in two genes (LSMAP and ABL1) were significantly associated with AP induced EPS (p < 0.001). These SNPs disrupt TFBS regulated byPOU2F1. Our results highlight the possible role of the disruption of TFBS by SNPs in the pharmacological response to AP.

A Cell-Based Method for Identification of Chemotherapy Resistance Cancer Genes.

Here we describe a method for identifying genes and genetic pathways responsible for chemoresistance in cancer cells. The method is based on generation and characterization of matched pairs of chemotherapy-sensitive/chemotherapy-resistant cancer cell lines. In this protocol we are using endometrial cancer cell lines treated with carboplatin and paclitaxel, which are first-line chemotherapies for gynecologic malignancies. The chemoresistant cells and their chemosensitive counterparts are used for downstream applications including bulk RNA-sequencing analysis to identify a set of genes and pathways that are associated with chemoresistance. Identification of pathways responsible for innate or acquired chemoresistance is of paramount importance for the identification of biomarkers for cancer risk stratification and prognosis, and as a pharmacogenomics model for identification of alternative chemotherapy approaches for treatment of patients with recurrent and chemoresistant disease.

Circulating Tumor and Invasive Cell Gene Expression Profile Predicts Treatment Response and Survival in Pancreatic Adenocarcinoma.

Previous studies have shown that pharmacogenomic modeling of circulating tumor and invasive cells (CTICs) can predict response of pancreatic ductal adenocarcinoma (PDAC) to combination chemotherapy, predominantly 5-fluorouracil-based. We hypothesized that a similar approach could be developed to predict treatment response to standard frontline gemcitabine with nab-paclitaxel (G/nab-P) chemotherapy. Gene expression profiles for responsiveness to G/nab-P were determined in cell lines and a test set of patient samples. A prospective clinical trial was conducted, enrolling 37 patients with advanced PDAC who received G/nab-P. Peripheral blood was collected prior to treatment, after two months of treatment, and at progression. The CTICs were isolated based on a phenotype of collagen invasion. The RNA was isolated, cDNA synthesized, and qPCR gene expression analyzed. Patients were most closely matched to one of three chemotherapy response templates. Circulating tumor and invasive cells’ SMAD4 expression was measured serially. The CTICs were reliably isolated and profiled from peripheral blood prior to and during chemotherapy treatment. Individual patients could be matched to distinct response templates predicting differential responses to G/nab-P treatment. Progression free survival was significantly correlated to response prediction and ΔSMAD4 was significantly associated with disease progression. These findings support phenotypic profiling and ΔSMAD4 of CTICs as promising clinical tools for choosing effective therapy in advanced PDAC, and for anticipating disease progression.

RNA Sequencing of Carboplatin- and Paclitaxel-Resistant Endometrial Cancer Cells Reveals New Stratification Markers and Molecular Targets for Cancer Treatment.

Despite advances in surgical technique and adjuvant treatment, endometrial cancer has recently seen an increase in incidence and mortality in the USA. The majority of endometrial cancers can be cured by surgery alone or in combination with adjuvant chemo- or radiotherapy; however, a subset of patients experience recurrence for reasons that remain unclear. Recurrence is associated with chemoresistance to carboplatin and paclitaxel and consequentially, high mortality. Understanding the pathways involved in endometrial cancer chemoresistance is paramount for the identification of biomarkers and novel molecular targets for this disease. Here, we generated the first matched pairs of carboplatin-sensitive/carboplatin-resistant and paclitaxel-sensitive/paclitaxel-resistant endometrial cancer cells and subjected them to bulk RNA sequencing analysis. We found that 45 genes are commonly upregulated in carboplatin- and paclitaxel-resistant cells as compared to controls. Of these, the leukemia inhibitory factor, (LIF), the protein tyrosine phosphatase type IVA, member 3 (PTP4A3), and the transforming growth factor beta 1 (TGFB1) showed a highly significant correlation between expression level and endometrial cancer overall survival (OS) and can stratify the 545 endometrial cancer patients in the TCGA cohort into a high-risk and low-risk-cohorts. Additionally, four genes within the 45 upregulated chemoresistance-associated genes are ADAMTS5, MICAL2, STAT5A, and PTP4A3 codes for proteins for which small-molecule inhibitors already exist. We identified these proteins as molecular targets for chemoresistant endometrial cancer and showed that treatment with their correspondent inhibitors effectively killed otherwise chemoresistant cells. Collectively, these findings underline the utility of matched pair of chemosensitive and chemoresistant cancer cells to identify markers for endometrial cancer risk stratification and to serve as a pharmacogenomics model for identification of alternative chemotherapy approaches for treatment of patients with recurrent disease.

Tumor-Derived Cell Lines as Molecular Models of Cancer Pharmacogenomics.

Compared with normal cells, tumor cells have undergone an array of genetic and epigenetic alterations. Often, these changes underlie cancer development, progression, and drug resistance, so the utility of model systems rests on their ability to recapitulate the genomic aberrations observed in primary tumors. Tumor-derived cell lines have long been used to study the underlying biologic processes in cancer, as well as screening platforms for discovering and evaluating the efficacy of anticancer therapeutics. Multiple -omic measurements across more than a thousand cancer cell lines have been produced following advances in high-throughput technologies and multigroup collaborative projects. These data complement the large, international cancer genomic sequencing efforts to characterize patient tumors, such as The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). Given the scope and scale of data that have been generated, researchers are now in a position to evaluate the similarities and differences that exist in genomic features between cell lines and patient samples. As pharmacogenomics models, cell lines offer the advantages of being easily grown, relatively inexpensive, and amenable to high-throughput testing of therapeutic agents. Data generated from cell lines can then be used to link cellular drug response to genomic features, where the ultimate goal is to build predictive signatures of patient outcome. This review highlights the recent work that has compared -omic profiles of cell lines with primary tumors, and discusses the advantages and disadvantages of cancer cell lines as pharmacogenomic models of anticancer therapies.

Pharmacogenomic Modeling in Pancreatic Cancer—Letter

Yu and colleagues ([1][1]) assessed the pharmacogenomics profiling (PGx) of circulating tumorigenic and invasive cells to stratify/predict treatment response in pancreatic ductal adenocarcinoma (PDAC). They suggested the PGx assay as an appropriate tool for choosing a potentially effective targeted

Pharmacogenomic modeling of circulating tumor and invasive cells for prediction of chemotherapy response and resistance in pancreatic cancer.

Despite a challenging prognosis, modern cytotoxic therapy can induce tumor responses and extend life in pancreatic adenocarcinoma (PDAC). Pharmacogenomic (PGx) modeling of tumor tissue can predict the efficacy of chemotherapeutic agents in preclinical cancer models. We hypothesized that PGx profiling of circulating tumor and invasive cells (CTIC) isolated from peripheral blood could predict tumor response, progression, and resistance. A PGx model was created and validated in preclinical models. A prospective clinical trial was conducted. Fifty patients with advanced PDAC were enrolled. Before treatment, 10 mL of peripherally drawn blood was collected. CTICs isolated from this blood sample were expression profiled and the PGx model was used to predict effective and ineffective chemotherapeutic agents. The treating physicians were blinded to PGx prediction. We found that CTICs could be reliably isolated, total RNA extracted and profiled from 10 mL of peripheral blood from patients with unresectable PDAC before chemotherapy treatment and at disease progression. Using previously created PGx models to predict chemotherapy sensitivity, we found that clinical benefit was seen for study participants treated with chemotherapy regimens predicted to be effective versus chemotherapy regimens predicted to be ineffective with regard to progression-free (10.4 mo vs. 3.6 mo; P < 0.0001; HR, 0.14) and overall survival (17.2 mo vs. 8.3 mo; P < 0.0249; HR, 0.29). These findings suggest that PGx profiling of CTICs can predict treatment response.

Genetic prediction of antidepressant drug response and nonresponse in Korean patients.

Genetic polymorphism contributes to variation in response to drug treatment of depression. We conducted three independent 6-week treatment studies in outpatients with major depressive disorder (MDD) to develop a pharmacogenomic model predicting response and nonresponse. We screened candidate genomic markers for association with response to selective serotonin reuptake inhibitors (SSRIs). No patients had received any antidepressant drug treatment in the current episode of depression. Outcome evaluation was blinded to drug and genotype data. The prediction model derived from a development sample of 239 completer cases treated with SSRIs comprised haplotypes and polymorphisms related to serotonin synthesis, serotonin transport, glutamate receptors, and GABA synthesis. The model was evaluated prospectively for prediction of outcome in a validation sample of 176 new SSRI-treated completer cases. The model gave a prediction in 60% of these cases. Predictive values were 85% for predicted responders and 86% for predicted nonresponders, compared to prior probabilities of 66% for observed response and 34% for observed nonresponse in those cases (both P<0.001). Convergent cross-validation was obtained through failure of the model to predict outcomes in a third independent sample of 189 completer cases who received non-SSRI antidepressants. We suggest proof of principle for genetic guidance to use or avoid SSRIs in a majority of Korean depressed patients.

Lymphoblastoid cell lines models of drug response: successes and lessons from this pharmacogenomic model.

A new standard for medicine is emerging that aims to improve individual drug responses through studying associations with genetic variations. This field, pharmacogenomics, is undergoing a rapid expansion due to a variety of technological advancements that are enabling higher throughput with reductions in cost. Here we review the advantages, limitations, and opportunities for using lymphoblastoid cell lines (LCL) as a model system for human pharmacogenomic studies. There are a wide range of publicly available resources with genome-wide data available for LCLs from both related and unrelated populations, removing the cost of genotyping the data for drug response studies. Furthermore, in contrast to human clinical trials or in vivo model systems, with high-throughput in vitro screening technologies, pharmacogenomics studies can easily be scaled to accommodate large sample sizes. An important component to leveraging genome-wide data in LCL models is association mapping. Several methods are discussed herein, and include multivariate concentration response modeling, issues with multiple testing, and successful examples of the 'triangle model' to identify candidate variants. Once candidate gene variants have been determined, their biological roles can be elucidated using pathway analyses and functionally confirmed using siRNA knockdown experiments. The wealth of genomics data being produced using related and unrelated populations is creating many exciting opportunities leading to new insights into the genetic contribution and heritability of drug response.

Pharmacogenomic modeling of pancreatic cancer (PDAC) for prediction of chemotherapy response and resistance in second-line treatment setting.

4130 Background: PDAC is a leading and rising cause of cancer mortality. Despite this, modern cytotoxics (C) can induce tumor responses and extend life. Xenograft models have shown that pharmacogenomic (PGx) modeling of C can predict efficacy. We previously presented results supporting PGx profiling of circulating tumor and invasive cells (CTICs) to predict effective C in the 1st line setting. We now present results in the second-line setting. Methods: PGx models for five common PDAC C regimens were created from the GI50 data obtained from the NCI-60 cell lines. These PGx models were validated in a prospective study of 50 patients with unresectable PDAC. 10 mL of peripheral blood was collected prior to C and at disease progression. CTICs were isolated, total RNA extracted and gene-expression analysis performed. Gene and pathway analyses were performed comparing patients with short v long TTP and for an individual patient at baseline v at time of progression. Results: CTICs were isolated, and gene expressi...

Pharmacogenomics and Cardiovascular Disease

Variability in drug responsiveness is a sine qua non of modern therapeutics, and the contribution of genomic variation is increasingly recognized. Investigating the genomic basis for variable responses to cardiovascular therapies has been a model for pharmacogenomics in general and has established critical pathways and specific loci modulating therapeutic responses to commonly used drugs such as clopidogrel, warfarin, and statins. In addition, genomic approaches have defined mechanisms and genetic variants underlying important toxicities with these and other drugs. These findings have not only resulted in changes to the product labels but also have led to development of initial clinical guidelines that consider how to facilitate incorporating genetic information to the bedside. This review summarizes the state of knowledge in cardiovascular pharmacogenomics and considers how variants described to date might be deployed in clinical decision making.

Use of pharmacogenomic modeling in pancreatic cancer for prediction of chemotherapy response and resistance.

4017 Background: Pancreatic adenocarcinoma (PDAC) is uniformly lethal and is the 4th leading cause of cancer mortality. Despite this, modern cytotoxics (C) can induce tumor responses and extend life. Xenograft models have shown that pharmacogenomic (PGx) modeling of C can predict efficacy. Chemo-sensitivity and gene expression profiling of circulating tumor and invasive cells (CTICs) isolated from peripheral blood may predict tumor response, progression and resistance. Methods: A prospective MSKCC study has completed planned accrual of 50 patients. 10 mL of peripheral blood is collected from patients with unresectable PDAC prior to C and at disease progression. Clinical data is prospectively collected. CTICs are isolated (Vita-Cap, Vitatex Inc.), total RNA is extracted and gene-expression analysis is performed. PGx models for twelve chemotherapy drug combinations used in PDAC were created from the GI50 data obtained from the NCI-60 cell lines (CellPath Therapeutics, Inc., Baltimore, MD). Expression data were normalized through GCRMA then probed to identify expression differences between patients with short v long TTP and within patients at baseline v at time of progression. The analysis was performed at the pathway and individual gene level. Results: CTICs were isolated, and gene expression and chemo-sensitivity patterns were obtained in all 50 patients prior to initiating C and in 20 patients at 1st line disease progression. Preliminary analysis demonstrates clinical benefit for patients treated with C predicted to be effective versus ineffective (TTP 7.3 mo v 3.7 mo, p=0.017, HR 0.30). Changes in chemo-sensitivity patterns were evident at disease progression, reflecting treatment resistance. Pathway analysis revealed that E2F1 and NFκB pathways are associated with prognosis, PLC and RB1 pathways become disrupted with disease progression. Conclusions: Isolation and gene expression profiling of CTICs can be performed reliably in unresectable PDAC. Preliminary analysis suggests that C profiling can predict response. Repeat PGx profiling identifies key pathways associated with treatment resistance. Clinical trial information: NCT01474564.

Use of pharmacogenomic modeling in pancreatic cancer for prediction of chemotherapy response and resistance.

142 Background: Pancreatic adenocarcinoma (PDAC) is uniformly lethal; despite this, modern cytotoxics (C) can induce tumor responses and extend life. Xenograft models have shown that pharmacogenomic (PGx) modeling of C can predict efficacy. Chemo-sensitivity and gene expression profiling of tumor progenitor (TP) cells isolated from peripheral blood may predict tumor response, progression and resistance. Methods: A prospective MSKCC study is ongoing with 46 of 60 planned patients accrued. 10 mL of peripheral blood is collected from patients with unresectable PDAC prior to C and at disease progression. Clinical data is prospectively collected. Circulating TP cells are isolated (Vita-Cap, Vitatex Inc.), total RNA is extracted and gene-expression analysis is performed. PGx models for twelve chemotherapy drug combinations used in PDAC were created from the GI50 data obtained from the NCI-60 cell lines (CellPath Therapeutics, Inc., Baltimore, MD). Expression data were normalized through GCRMA then probed to identify expression differences between the 35 patients with continued disease response and the 11 patients with disease progression. Analysis was performed at the pathway and individual gene level. Results: TP cells were isolated and gene expression and chemo-sensitivity patterns were obtained in all 46 evaluable patients prior to initiating C and in all 11 patients at 1st line disease progression. Preliminary analysis demonstrates clinical benefit for patients treated with C predicted to be effective versus C predicted to be ineffective (TTP 4.4 mo versus 2.7 mo, p=0.016). Changes in chemo-sensitivity patterns were evident at disease progression, reflecting treatment resistance. Hedgehog pathway over-expression was associated with resistance to gemcitabine but clinical response to 5-FU based treatment. Pathway analysis revealed that ErbB3, ARE/Nrf2 and Insulin pathways distinguished patients with disease progression. Conclusions: Isolation and gene expression profiling of TP cells can be performed reliably in unresectable PDAC. Preliminary analysis suggests that C profiling can predict response. Repeat PGx profiling identifies key pathways associated with treatment resistance. Clinical trial information: NCT01474564.

Using Genetic and Clinical Factors to Predict Tacrolimus Dose in Renal Transplant Recipients

Tacrolimus has a narrow therapeutic window and shows significant interindividual difference in dose requirement. In this study we aim to first identify genetic factors that impact tacrolimus dose using a candidate gene association approach, and then generate a personalized algorithm combining identified genetic and clinical factors to predict individualized tacrolimus dose. We screened 768 SNPs in 15 candidate genes in metabolism, transport and calcineurin inhibition pathways of tacrolimus, for association with tacrolimus dose in a discovery cohort of 96 patients. Four polymorphisms in CYP3A5 and one polymorphism in CYP3A4 were identified to be significantly associated with tacrolimus stable dose (p < 8.46 × 10(-5)). The same SNPs were identified when dose-normalized trough tacrolimus concentration was analyzed. The CYP3A5*1 allele was associated with significantly higher stable dose, bigger dose increase, higher risk of being underdosed and lower incidence of post-transplant hyperlipidemia. ABCB1 polymorphisms were not associated with stable dose. No significant difference was found between CYP3A5 expressers and nonexpressers in incidence of acute rejection and time to first rejection. Age, ethnicity and CYP3A inhibitor use could predict 30% of tacrolimus dosing variability. Adding the identified genetic polymorphisms to the algorithm increased the predictability to 58%. In two validation cohorts of 77 and 64 patients, the algorithm containing both genetic and clinical factors produced correlation coefficients of 0.63 and 0.42, respectively. This algorithm gave a prediction of the stable doses closer to the actual doses when compared with another algorithm based only on the CYP3A5 genotype. CYP3A5 genotype is the most significant genetic factor that impacts tacrolimus dose among the genes studied. This study generated the first pharmacogenomics model that predicts tacrolimus stable dose based on age, ethnicity, genotype and comedication use. Our results highlight the importance of incorporating both genetic and clinical, demographic factors into dose prediction.

Validation of VKORC1 and CYP2C9 genotypes on interindividual warfarin maintenance dose: a prospective study in Chinese patients

To develop a warfarin-dosing algorithm that could be combined with pharmacogenomic and demographic factors, and to evaluate its effectiveness in a randomized prospective controlled clinical trial. A pharmacogenetics-based dosing model was derived using retrospective data from 266 Chinese patients and multiple linear regression analysis. To prospectively validate this model, 156 patients with an operation of heart valve replacement were enrolled and randomly assigned to the group of pharmacogenetics-guided or traditional dosing for warfarin therapy. All patients were followed up for 50 days after initiation of warfarin therapy. The log-rank test was compared with the time-to-event (Kaplan-Meier) curves. Cox proportional hazards-regression model was used to assess the hazard ratio of the time to reach stable dose. The linear regression model derived from the pharmacogenomic model correlated with 54.1% of warfarin dosing variance. The final multiple linear regression model included age, body surface area, VKORC1, and CYP2C9 genotype. The study showed that the hazard ratio for the time to reach stable dose was 1.932 for the traditional dosing group versus the model-based group and a close and highly significant relationship was observed to exist between the predicted and the actual warfarin dose (R=0.454). A pharmacogenetics-based dosing algorithm has been developed for improvement in the time to reach the stable dosing of warfarin. This model may be useful in helping the clinicians to prescribe warfarin with greater safety and efficiency.