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Associated Trial(s):- NCT00540449 - A Phase III, Randomized, Double-blind Trial of TMC278 25 mg q.d. Versus Efavirenz 600mg q.d. in Combination With a Fixed Background Regimen Consisting of Tenofovir Disoproxil Fumarate and Emtricitabine in Antiretroviral-naive HIV-1 Infected Subjects
- NCT00543725 - A Phase III, Randomized, Double-blind Trial of TMC278 25mg q.d. Versus Efavirenz 600mg q.d. in Combination With a Background Regimen Containing 2 Nucleoside/Nucleotide Reverse Transcriptase Inhibitors in Antiretroviral-naive HIV-1 Infected Subjects.
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Status: Withdrawn/ClosedResearch Proposal
Project Title: A Second Life for Rilpivirine
Scientific Abstract:
Background: Bladder cancer poses a significant medical challenge marked by high recurrence rates, especially in superficial tumors, necessitating adjuvant therapy. Drug repurposing, an innovative approach, is crucial, with Rilpivirine, a drug used in antiretroviral therapy, emerging as a promising candidate due to its potential affordability and streamlined development.
Objective: The primary objective is to develop an in-silico model using Physiologically Based Pharmacokinetic (PBPK) software, such as Simulation Plus, to explore the potential repurposing of Rilpivirine for various cancer types, with a specific emphasis on bladder cancer.
Study Design: The study employs a multidimensional approach, integrating population data from healthy individuals and those affected by cancer, physicochemical properties of the drug, enzymatic information, and clinical trial data from the YODA project. PBPK studies are conducted to develop new models for the repurposing of Rilpivirine.
Participants: The target population includes all patients with sufficient data points for retrospective analysis, ensuring the representation of diverse demographic and clinical characteristics. Eligible participants are those who have undergone a treatment regimen incorporating Rilpivirine.
Primary and Secondary Outcome Measures: The primary outcome measure is the development of an in-silico model for the repurposing of Rilpivirine in cancer treatment, particularly focusing on bladder cancer. Secondary outcome measures encompass optimal dosage regimens, exploration of drug-drug interactions, mechanistic studies, correlation analysis, and the evaluation of model performance.
Statistical Analysis: Statistical analysis involves accessing participant-level data through a secure platform, utilizing analytical tools such as GastroPlus, GraphPad and Open Office. The analysis plan includes descriptive statistics, data preprocessing, inferential statistics, model development and evaluation, sensitivity analysis, and validation.
Brief Project Background and Statement of Project Significance: Bladder cancer starts when cells that make up the urinary bladder start to grow out of control. As more cancer cells develop, they can form a tumor and, with time, spread to other parts of the body (2). The majority of patients have a superficial disease that can be managed with surgery. However, superficial tumors recur in 65% of patients, and ∼30% of these tumors progress to a higher grade or stage, necessitating adjuvant therapy (3). In the quest to develop innovative therapy methods, repurposing drugs like Rilpivirine emerges as a promising avenue, offering potential benefits of affordability and expedited development. Within this context, the research group envisions a future where repurposed drugs could revolutionize cancer treatment, potentially leading to life-saving outcomes. By leveraging in silico modulation as an initial step, this approach optimizes resource utilization by reducing the need for extensive laboratory materials and minimizing animal sacrifice. In silico modulation empowers researchers to efficiently explore vast combinations and concentrations of repurposed drugs, enabling the identification of those with the highest potential for combating cancer. Consequently, ineffective drug candidates can be swiftly excluded from further study, streamlining the research process and paving the way for the development of more targeted and effective cancer treatments. Ultimately, this approach holds great promise in saving lives while optimizing resources and expediting the discovery of novel therapies.
Specific Aims of the Project:
The primary objective of this project is to develop an in-silico model utilizing PBPK (Physiologically Based Pharmacokinetic) software, such as Simulation Plus, to explore the potential of repurposing antiretroviral drugs, specifically Rilpivirine, for the treatment of various types of cancer. To achieve this aim, a multidimensional approach will be employed, integrating population data encompassing both healthy individuals and those affected by the disease, physicochemical properties of the drug, enzymatic information, and clinical trial data obtained from the YODA project. The in-silico models developed in this study will provide crucial insights into several key aspects, including dosage determination, drug-drug interactions, and the pharmacokinetics of Rilpivirine in cancer patients. By leveraging population data of the clinical trials provided by YODA project and incorporating diverse parameters, the models will offer valuable information on how Rilpivirine behaves within different cancer types and patient populations. These models will simulate the drug's distribution, metabolism, and elimination processes within the human body, providing a comprehensive understanding of its pharmacokinetic profile specifically in the context of cancer treatment.
To ensure the accuracy and reliability of the in-silico models, extensive validation will be conducted. This validation process will involve utilizing cancer cell models, which replicate the characteristics of specific cancer types, to assess the model's predictive capabilities. By exposing these cell models to Rilpivirine and comparing the outcomes with the predictions made by the in-silico model, researchers can ascertain the model's accuracy in capturing the drug's behavior within cancer cells. In addition, mechanistic studies will be performed to delve into the underlying mode of action of Rilpivirine in the context of cancer treatment. These studies will encompass investigations into apoptotic pathways, clonogenic potential, and the quantification of RNA and protein expression related to the drug's effects on cancer cells. By combining in-silico modeling obtained from the YODA project data, validation using cancer cell models, and mechanistic studies, this research project seeks to establish a comprehensive understanding of Rilpivirine's potential as a repurposed drug for cancer treatment. The insights gained from this study will contribute to the development of personalized treatment strategies, optimize dosage regimens, and provide valuable knowledge about the mechanisms underlying Rilpivirine's efficacy in combating cancer. Ultimately, the goal is to advance the field of cancer therapeutics and potentially offer new treatment options that can improve patient outcomes.
Study Design:
Meta-analysis (analysis of multiple trials together)
Explain:
The trials will be used together to develop new models for drug repurposing of Rilpivirine.
What is the purpose of the analysis being proposed? Please select all that apply.: Confirm or validate previously conducted research on treatment effectiveness Develop or refine statistical methods Research on clinical prediction or risk prediction
Software Used: R
Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study:
The target population for this study encompasses all patients who possess sufficient data points for a retrospective analysis. In order to account for the diverse characteristics and requirements of different patient groups, various models will be constructed based on specific criteria, such as age and gender. By considering these factors, the researchers can gain deeper insights into the effects of Rilpivirine within specific subpopulations.
Patients will be eligible for inclusion in the study if they have undergone a treatment regimen that incorporates Rilpivirine. This inclusive approach ensures that a broad range of patients who have been exposed to Rilpivirine are considered, allowing for a comprehensive analysis of its impact. By encompassing patients from different backgrounds and medical conditions, the study aims to capture the diversity of responses to Rilpivirine and identify potential patterns or correlations.
Exclusion criteria include:
• Insufficient data availability: patients with incomplete or insufficient data for a comprehensive retrospective analysis
• Lack of Relevant Clinical Information: patients lacking crucial clinical information necessary for the development and validation of in-silico models, including pharmacokinetic parameters and clinical trial data.
• Non-compliance with Treatment Regimen: patients with a history of non-compliance with the prescribed Rilpivirine treatment regimen may be excluded to ensure the reliability of the data.
• Adverse events impacting data validity: patients experiencing severe adverse events that could potentially impact the validity of the data, such as those leading to treatment interruptions.
• Inadequate clinical trial data quality: patients with significant data quality issues, such as unreliable or inconsistent information.
Primary and Secondary Outcome Measure(s) and how they will be categorized/defined for your study:
Primary Outcome Measure: an in-silico model for the repurposing of Rilpivirine as a potential treatment for various types of cancer, with a specific focus on bladder cancer. The model will encompass multiple aspects, including drug pharmacokinetics (PK), drug-drug interactions, optimal dosages, and potential combination effects.
Secondary Outcome Measures:
Dosage Determination: optimal dosage regimens of Rilpivirine for cancer treatment.
Drug-Drug Interactions: interactions between Rilpivirine and other drugs, both anticancer agents and other repurposed drugs, potential synergistic or antagonistic effects of drug combinations.
Mechanistic Studies: underlying mechanisms of Rilpivirine's effects on cancer cells, including pathways related to apoptosis, clonogenic potential, and gene/protein expression, can provide valuable insights into its mode of action as a potential anticancer agent.
Correlation Analysis: Correlations between drug concentrations, demographic variables (such as age, gender), and other relevant clinical parameter.
Model Performance Evaluation: performance of the developed PK/PBPK model
Main Predictor/Independent Variable and how it will be categorized/defined for your study:
Demographic information: Collect demographic data from the clinical trials, including age, ethnicity, and other relevant factors that may influence the outcomes or treatment approach, such as diseases that patients have.
Dosing regimens: Gather information on the dosing regimens of Rilpivirine administered to patients. This includes details such as dosage forms, dosages, frequency of administration, and duration of treatment, as well as other drugs used in combination.
Laboratory results: Extract relevant laboratory results from the clinical trials, such as blood tests, and any other diagnostic tests performed during the course of treatment.
Pharmacokinetic properties of antiviral drugs: Collect data on the pharmacokinetic properties of antiviral drugs used in the treatment of patients. This includes information on drug concentrations in plasma through different time points, tissues, and organs, clearance, volume of distribution, and half-life.
Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study:
Genetic Factors: Genetic information related to drug metabolism and response. Genetic variations can influence how patients respond to drugs, including Rilpivirine.
Disease Biomarkers: Biomarkers specific to the cancer type, indicating disease progression or response to treatment. This variable provides insights into the drug's impact on the disease.
Adverse Events: Information on any adverse events experienced by patients during treatment. This variable helps assess treatment safety and tolerability.
Statistical Analysis Plan:
Descriptive Statistics: Calculate summary statistics (mean, median, standard deviation, etc.) for demographic variables such as age, and other relevant characteristics of the study population; Summarize the distribution of drug concentrations in plasma, tissues, and organs.
Data Preprocessing: Check for missing values, outliers, and inconsistencies in the dataset; Handle missing data through imputation or exclusion based on predefined criteria; Identify and address any data quality issues or discrepancies through data cleaning and verification.
Inferential Statistics: Perform hypothesis testing to assess associations between demographic variables (e.g., age, gender) and drug concentration using appropriate statistical tests (e.g., chi-square test, t-test, ANOVA); Explore correlations between drug concentrations and other relevant variables using correlation analysis (e.g., Pearson correlation coefficient). Assess the significance and magnitude of observed associations using appropriate statistical tests (e.g., linear regression, logistic regression).
Model Development and Evaluation: Use software of Simulation Plus or other modeling tools to develop PK/PBPK models based on the collected data; Apply covariate selection methods (e.g., COSSAC) to estimate model parameters and assess the influence of demographic and clinical variables on drug pharmacokinetics; Evaluate model performance using statistical tests such as goodness-of-fit criteria, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and visual diagnostics (e.g., residual analysis).
Sensitivity Analysis and Validation: Conduct sensitivity analysis to assess the robustness of the PK/PBPK models by varying input parameters and assessing their impact on model predictions; Validate the developed models using a validation dataset to assess their generalizability and predictive performance. Compare the model predictions with observed data and assess the accuracy, precision, and reliability of the models.
Narrative Summary: We're investigating whether a drug called Rilpivirine, commonly used for treating HIV, can also be used to treat certain types of cancer like lung, bladder, and prostate cancer. This is an exciting idea because repurposing existing drugs for new uses can save time and money in developing new treatments. In our study, we'll first use computer models to predict how Rilpivirine could work against cancer cells. We'll use information about the drug's properties and how it behaves in the body, in combination with the data obtained from YODA project. Then, we'll test these predictions in the lab using cancer cell lines. This could help us figure out if Rilpivirine has potential as a cancer treatment without having to do extensive testing in humans right away. If successful, this approach could lead to new and effective cancer treatments that are more affordable and quicker to develop.
Project Timeline:
This study is an integral part of a doctoral program with a fixed duration of three years. The project is poised to commence as soon as the necessary data is made available, given that all the required software is prepared and ready for use. The anticipated timeline for this research project involves distinct phases that span the course of the doctoral program.
The initial phase of the study will entail data analysis and the development of a physiologically based pharmacokinetic (PBPK) model. This phase is estimated to require approximately 12 months to complete. Throughout this period, the researchers will diligently analyze the provided data, extract relevant information, and employ statistical techniques to uncover meaningful insights. Concurrently, they will work on developing a sophisticated PBPK model that captures the dynamics of drug distribution, metabolism, and excretion within the human body. This model will serve as a vital tool for subsequent stages of the research. As the data analysis and PBPK model development progress, the researchers will work on drafting manuscripts to document their findings and insights. These manuscripts will be prepared simultaneously, ensuring that the project's progress is disseminated through scientific publications.
Following the completion of the data analysis and PBPK model development phase, the subsequent stage of the study will involve the validation of the model using in vitro experiments and mechanistic studies employing cancer cell lines. This validation process and the associated mechanistic studies are anticipated to occupy the remaining 24 months of the doctoral program. During this period, the researchers will conduct a series of experiments using cancer cell lines to verify the accuracy and reliability of the PBPK model. These experiments will involve assessing the response of the cancer cell lines to various concentrations and combinations of repurposed drugs, such as Rilpivirine, as predicted by the PBPK model. The outcomes of these experiments will be meticulously recorded, analyzed, and compared to the predictions of the model, facilitating the evaluation and refinement of the model's performance.
Dissemination Plan: The results from this study will go towards the doctoral thesis of the student and will also be published in journals such as Pharmaceutics, European Journal of Pharmacology and journal of the American Association of Pharmaceutical Scientists.
Bibliography:
1. Pushpakom S, Iorio F, Eyers PA, Escott KJ, Hopper S, Wells A, et al. Drug repurposing: progress, challenges and recommendations. Nature Reviews Drug Discovery. 2019;18(1):41-58.
2. Link (28-11-2020): https://www.cancer.org/cancer/bladder-cancer/about/what-is-bladder-cancer.html
3. Cote, R. J. & Datar, R. H. Therapeutic approaches to bladder cancer: identifying targets and mechanisms. Crit Rev Oncol Hematol. 46 Suppl, S67–83 (2003).