Predictors of exposure, therapeutic and adverse effects of medicines used in the treatment of multiple myeloma

Lead Investigator: Ahmad Abuhelwa, University of Sharjah
Title of Proposal Research: Predictors of exposure, therapeutic and adverse effects of medicines used in the treatment of multiple myeloma
Vivli Data Request: 8427
Funding Source: None
Potential Conflicts of Interest: None

Summary of the Proposed Research:

Multiple myloma (MM) is a cancer of plasma cells and is on the rise worldwide, particularly in the US, Australia, and Western Europe. Nearly 2% of cancer diagnoses and more than 2% of cancer deaths in the US are due to MM. Since 1990, the global incidence of MM has increased by 126% and US incidence by over 40%, whilst global mortality has increased by 94%.
There are many important medicines used in the treatment of multiple myeloma (MM) including ixazomib, daratumumab, and isatuximab. Ixazomib is a proteasome inhibitor which is a drug that inhibit the cellular protein called ‘proteosome’ leading to potein build up and killing of myeloma cells. Daratumumab and isatuximab are monoclonal antibodies are medicines created in the lab and act in a simular fashion to the body’s own antibodies where they help the immune system to recognize and block a specific target. However, response and toxicity to these drugs are highly variable and there are no accurate and reliable tools to date to accurately predict patients who will benefit most. Using the diverse range of data collected from MM clinical trials, including individual’s demographic, clinical, laboratory, disease and genetic characteristics, it is possible to identify predictors and develop clinical tools that enable improved prediction of therapeutic and adverse outcomes from MM medicines. Being able to identify the expected response and adverse effect profile may enable patients and clinicians to make better decisions regarding whether to commence, continue, or change dosing of medicines used in the treatment of MM.

Statistical Analysis Plan:

Population:
Patients with MM treated with daratumumab, ixazomib, or isatuximab and relevant comparator arms. The project will include the following YODA studies: NCT02252172, NCT02076009, NCT02136134, NCT01985126, and NCT00574288. A separate request has been submitted to YODA for these studies to be shared through Vivli (YODA request: 2022-5048).

Study design:
A observational cohort analysis of available clinical trial individual-participant data will be undertaken to identify and develop clinically predictive markers and tools of response and adverse effects in patient with MM who have initiated daratumumab/ixazomib/isatuximab.

Software:
The R Software (R Core Team) will be used for data preparation, modelling and graphical output.

Data:
Data are required for the outcomes including response (early, depth, best overall), overall survival, progression-free survival, adverse event outcomes (clinician / patient reported adverse effects that have been defined according to the international common toxicity criteria, and adverse events requiring medication changes), and drug exposure (concentration).

Through this project we will publish works identifying baseline and on-treatment predictors and develop clinical prediction models of the key therapeutic and adverse outcomes of medicines used in the treatment of MM. Potential predictors will be prioritised according to biological/clinical plausibility and prior evidence of association with the relevant outcome (i.e., adverse events or therapeutic response). These domains will be explored as to whether they identify therapeutic and adverse outcomes for all patients with MM or are specific to individual treatments. Samples sizes are well within scope to investigate and publish information across these degrees of freedom. It is anticipated, but will be informed by analyses, that several publications may arise from this project, including for the pooled population or for predictors identified as specific to individual treatments.

As most of the data collected within a clinical trial contains some information on the immune system, disease severity and prognosis, toxicity risk or drug exposure, it is important to have access to all the baseline and follow-up clinical/biological/patient characteristic data collected on an individual during a trial.

General model development:
Cox-proportional hazard / time-to-event models will be used to assess the association between potential predictors and the time to an adverse effect or survival time. Associations will be reported primarily as hazard ratios with 95% confidence intervals. The association of potential predictors with binary outcomes (e.g., best overall response) will be modelled using logistic regression and will be reported as odds ratios with 95% confidence intervals. Longitudinal analysis (e.g., linear, and non-linear mixed effect modelling) will be used to assess the nature and patterns of longitudinal changes of key continuous variables (e.g., drug concentration, neutrophil counts).

Crude associations will be reported based on univariate analysis (adjusting only for the study population and treatment arm). Analyses will be adjusted for individual study populations and treatment arms and adjustment for confounders will be done in the multivariable analysis. Continuous variables will be assessed for non-linearity of association with outcomes using restricted cubic splines. Should multiple values of an assessed covariate be recorded for a single visit (e.g. blood pressure) the mean of the multiple reads will be used. The varying performance of clinical prediction models developed using multivariable analysis techniques including stepwise regressions, penalised methods [which minimise the risk of overfitting (e.g., elastic net analysis)], and machine learning [which excel in optimising prediction with high-dimensional data (e.g., random forest and gradient boosted methods)] will be assessed. Early markers of exposure, response and toxicity will be evaluated using a landmark approach where possible, with sensitivity analyses based on the use of time-dependent covariates. Landmark time will be dependent on the time points available in individual studies, and the time frame of changes in each specific predictor variable. As our analyses are primarily hypothesis generating and they will require subsequent validation, no formal adjustment for multiple testing is intended. However, this limitation will be clearly stated in any publications of results. As it is expected that < 5% of data will be missing for assessed variables, complete case analyses are planned. Should variables with substantial missing data be present, the pattern and likely cause of the missing data will be evaluated and if missing at random is reasonable to assume then single regression imputation will be undertaken.
Analyses will include evaluating the heterogeneity in outcome predictors for MM drugs as compared to relevant comparator. Such evaluations will allow a better understanding of whether the relationships identified are specific to a specific MM drug or are common to standard treatment (i.e., common prognostic factors). Where comparisons between treatments are made we will maintain the validity of randomized design as is typical to traditional subgroup analyses of RCT data.

Power:
Predictors that have a clinically meaningful (e.g., double the risk) effect on mortality and adverse effects will be of primary interest. Based upon a 30% incidence of toxicity, a sample size of approximately 600 is required to detect a predictor (with a 10% frequency within the population) associated with a two-fold risk (α=0.05 with 80% power). Based upon an event rate of 40% during trial follow-up (e.g., for progression), approximately 450 participants are required for 80% power to detect a predictor (with a 10% frequency within the population) associated with a two-fold hazard of the event (α=0.05). Reference: Chow S, Shao J, Wang H. 2008. Sample Size Calculations in Clinical Research. 2nd Ed. Chapman & Hall/CRC Biostatistics Series. page 177. These samples sizes are well within scope for this study. Sample sizes greater than this will allow the investigation of more complex relationships with greater predictive performance, which is a primary objective of this study.

Quality Control:
Data will be explored for inconsistencies in time recordings, physiologically unreasonable covariate values, and unit errors. Prior to beginning analyses, individual data values will be extracted/constructed based on the raw and analysis datasets provided. To ensure that each variable has been correctly extracted/constructed from the data provided, basic analyses and descriptive statistics will be reproduced to check for consistency with pertinent results in published manuscripts or clinical study reports (CSRs) relating to the specific trial. Where there are insufficient published results to confirm the proper extraction of the variable, the extracted values will be manually checked against a random sample of the original dataset values.

Requested Studies:

A Phase 3 Randomized, Open-label, Multicenter Study Comparing Isatuximab (SAR650984) in Combination With Pomalidomide and Low-Dose Dexamethasone Versus Pomalidomide and Low-Dose Dexamethasone in Patients With Refractory or Relapsed and Refractory Multiple Myeloma
Data Contributor: Sanofi
Study ID: NCT02990338
Sponsor ID: EFC14335

A Phase 3, Randomized, Double-Blind, Multicenter Study Comparing Oral Ixazomib (MLN9708) Plus Lenalidomide and Dexamethasone Versus Placebo Plus Lenalidomide and Dexamethasone in Adult Patients With Relapsed and/or Refractory Multiple Myeloma
Data Contributor: Takeda
Study ID: NCT01564537
Sponsor ID: C16010

A Phase 3, Randomized, Placebo-Controlled, Double-Blind Study of Oral Ixazomib Maintenance Therapy After Initial Therapy in Patients With Newly Diagnosed Multiple Myeloma Not Treated With Stem Cell Transplantation
Data Contributor: Takeda
Study ID: NCT02312258
Sponsor ID: C16021

An Open-Label, Phase 2 Study to Evaluate the Oral Combination of Ixazomib (MLN9708) With Cyclophosphamide and Dexamethasone in Patients With Newly Diagnosed or Relapsed and/or Refractory Multiple Myeloma Requiring Systemic Treatment
Data Contributor: Takeda
Study ID: NCT02046070
Sponsor ID: C16020

A Phase 3, Randomized, Double-Blind, Multicenter Study Comparing Oral MLN9708 Plus Lenalidomide and Dexamethasone Versus Placebo Plus Lenalidomide and Dexamethasone in Adult Patients With Newly Diagnosed Multiple Myeloma
Data Contributor: Takeda
Study ID: NCT01850524
Sponsor ID: C16014

Daratumumab (HuMax®-CD38) Safety Study in Multiple Myeloma – Open Label, Dose-escalation Followed by Open Label, Single-arm Study
Data Contributor: Johnson & Johnson
Study ID: NCT00574288
Sponsor ID: CR101876

An Open-label, Multicenter, Phase 2 Trial Investigating the Efficacy and Safety of Daratumumab in Subjects With Multiple Myeloma Who Have Received at Least 3 Prior Lines of Therapy (Including a Proteasome Inhibitor and IMiD) or Are Double Refractory to a Proteasome Inhibitor and an IMiD
Data Contributor: Johnson & Johnson
Study ID: NCT01985126
Sponsor ID: CR102651

Phase 3 Study Comparing Daratumumab, Bortezomib and Dexamethasone (DVd) vs Bortezomib and Dexamethasone (Vd) in Subjects With Relapsed or Refractory Multiple Myeloma
Data Contributor: Johnson & Johnson
Study ID: NCT02136134
Sponsor ID: CR103995

Phase 3 Study Comparing Daratumumab, Lenalidomide, and Dexamethasone (DRd) vs Lenalidomide and Dexamethasone (Rd) in Subjects With Relapsed or Refractory Multiple Myeloma
Data Contributor: Johnson & Johnson
Study ID: NCT02076009
Sponsor ID: CR103663

A Phase 3 Study Comparing Daratumumab, Lenalidomide, and Dexamethasone (DRd) vs Lenalidomide and Dexamethasone (Rd) in Subjects With Previously Untreated Multiple Myeloma Who Are Ineligible for High Dose Therapy
Data Contributor: Johnson & Johnson
Study ID: NCT02252172
Sponsor ID: CR104762

Public Disclosures:

Almansour, S.A., Alqudah, M.A., Abuhelwa, Z., Al-Shamsi, H.O., Alhuraiji, A., Semreen, M.H., Bustanji, Y., Alzoubi, K.H., Modi, N.D., Mckinnon, R.A. and Sorich, M.J., 2024. Antithrombotic utilization, adverse events, and associations with treatment outcomes in multiple myeloma: pooled analysis of three clinical trials. Therapeutic Advances in Medical Oncology, 16, p.17588359241275387. Doi : 10.1177/17588359241275387
Almansour, S.A., Alqudah, M.A., Abuhelwa, Z., Al-Shamsi, H.O., Semreen, M.H., Bustanji, Y., Soare, N.C., McKinnon, R.A., Sorich, M.J., Hopkins, A.M. and Abuhelwa, A.Y., 2024. Association of proton pump inhibitor use with survival and adverse effects outcomes in patients with multiple myeloma: pooled analysis of three clinical trials. Scientific Reports, 14(1), p.591. Doi: 10.1038/s41598-023-48640-1