Prior Efforts

CP-SCD created a regulatory science strategy to de-risk drug development for the benefit of individuals living with sickle cell disease (SCD). The CP-SCD consortium brought together a wide range of SCD stakeholders, including partners from the pharmaceutical industry, academic institutions, regulatory agencies, and patient-advocacy organizations to accelerate treatments and therapies for SCD.

The consortium collaborated with C-Path’s Rare Disease Cures Accelerator-Data and Analytics Platform, the Quantitative Medicine Program, the Patient-Reported Outcome Consortium, the Regulatory Science program, and others to accomplish its mission. CP-SCD analyzed clinical trial data, created mathematical models that predicted disease development, identified novel biomarkers, and provided regulatory solutions for the pharmaceutical industry that could make clinical trials more rapid, less expensive and increase the success rate, thereby making new therapies available to patients with SCD sooner.

CP-SCD collected, curated, and analyzed clinical datasets. At the conclusion of the consortium, CP-SCD accumulated six datasets of clinical data from over 6,000 patients. These data enabled the program to contribute to gain insights for drug development for people living with SCD.

CPTR was a public‐private partnership initiated in March of 2010 by Critical Path Institute (C-Path), the Bill & Melinda Gates Foundation (BMGF) and the Global Alliance for TB Drug Development (TB Alliance). This collaboration included participation from the pharmaceutical industry, academia, as well as national and global government agencies, in order to develop and integrate data standards, qualify biomarkers through FDA/EMA, develop quantitative disease progression (natural history) models, generate disease response metrics, develop supporting assays for biomarkers, and develop new pharmacokinetic/dynamic models, as well as innovative metrics for potential drug-drug interactions.

CPTR also developed a quantitative drug safety tool to optimize the translational prediction of the probability of novel anti-TB regimens of inducing a potentiallyh life-threatening abnormal heart rhythm ( torsades de pointes or TdP).

Additionally, CPTR developed in vivo pharmacokinetic/pharmacodynamics evidence-based evaluation of animal model predictive accuracy effort: quantifying the translatability of preclinical data to optimize clinical trial design.

CPTR had several projects that reflected contributions to the design of active control trials. It quantified the link between the dynamic changes in time to positivity, as measured by liquid culture in Phase II studies with clinically relevant endpoints in Phase III trials. In addition, CPTR’s mechanistic systems pharmacology model for TB quantified the interaction between bacteria, immune system and drug treatments. CPTR’s Hollow Fiber System for TB (HFS-TB) is qualified by EMA and used to predict clinically relevant findings based on in vitro experiments. Finally, CPTR’s model-based meta-analysis of Phase III quinolone trials is quantifying the relationship between endpoints in these large Phase III trials to evaluate novel anti-TB regimens.

CPTR’s liquid culture effort analyzed the predictive accuracy of TB time-to-positivity and two-month culture conversion based on data from three recently completed trials to inform decisions when moving from Phase II to Phase III clinical trials.

C-Path’s CPTR initiative evaluated and sponsored validation of several drug development tools for TB, including a hollow fiber in vitro testing system to measure efficacy of new combination therapies for TB (this tool was qualified by EMA on January 22, 2015), and quantified liquid culture as prognostic indication of relapse. CPTR launched a a relational sequencing data platform for TB (ReSeqTB), to inform the development of new rapid drug sensitivity tests for TB to enable the implementation of new drugs and drug regimens. CPTR developed several models, from a physiologically based pharmacodynamic model to a population-based disease progression model.

CFAST was an initiative formed in June 2012 to accelerate clinical research and therapeutic developments by creating and maintaining data standards, tools and methods for conducting research in therapeutic areas that are important to public health. CFAST was formed as a partnership between the Clinical Data Interchange Standards Consortium (CDISC) and the Critical Path Institute (C-Path). Since launching CFAST, CDISC and C-Path have worked together to develop and publish new data standards for therapeutic areas and corresponding user guides.

CDISC and C-Path have agreed to discontinue using the separate CFAST brand, but they both remain committed to this mission and continue to partner to develop and publish therapeutic area data standards.

For additional information on published Therapeutic Area (TA) Standards, please visit CDISC.

More information can also be found on C-Path’s Data Collaboration Center here.

Launched in December, 2012 MSOAC was a dynamic partnership formed to promote consensus science. Created jointly with the National Multiple Sclerosis Society, MSOAC collected, standardized, and analyzed data about MS with the goal of qualifying a new measure of disability as a primary or secondary endpoint for future trials of MS therapies.

The National Multiple Sclerosis Society recognized the gap in the MS treatment pipeline and decided to employ a method that has steadily gained support within the research community — consortia science. MSOAC brings stakeholders from industry, academia, MS advocacy groups, and regulatory agencies together to spur development of drug development tools to assess the effectiveness of potential treatments for all forms of MS.

Pediatric studies conducted in the last 20 years, spurred by US and EU legislation, have provided critically important information to guide clinical care for children. However, similar challenges remained. To help address this, PTC was founded in 2015, with the aim of establishing a dedicated non-profit that focused on a transformative clinical trials network for pediatrics. This led to the incorporation of iACT in March of 2017, and the long-standing collaboration between iACT and C-Path.

The PredicTox Knowledge Environment (KE) advanced the science of adverse event prediction by creating an integrated web-based knowledge environment. PredicTok KE consisted of multiple interconnected databases that capture quantitative multiscale biology of drug action, the PredictTox-KE. The databases will contain information on clinical indications, preclinical physiological data, and cellular regulatory networks. Although many datasets relevant to drug-induced toxicities currently exist, the advantage of a centralized repository is that data can be normalized to ontologies to ensure consistent definitions. As such, all data in PredictTox-KE will be organized within a computable framework that enables integration and analysis.

Creation of the PredicTox-KE enabled an integrative approach to prediction of major adverse events associated with therapeutic agents. This will be done as a collaborative project between researchers at Icahn School of Medicine at Mount Sinai (ISMMS) and Critical Path Institute (C-Path). Data from academia, industry and publicly available databases will be integrated, and querying tools and analytical models will be developed to enable predictive toxicology. In this initial two-year demonstration project, we propose the creation of an initial proof of concept database containing clinical, cellular, and animal data related to adverse events (i.e. cardiomyopathy) caused by small-molecule (the “NIB” class; e.g. imatinib, sorafenib, sunitinib) or antibody (the “MAB” class; e.g. trastuzamab) protein kinase inhibitors used to treat cancer. Although these targeted therapeutics are being successfully used to treat several cancers, mechanisms underlying cardiotoxicity are poorly understood, which makes improved prediction of these serious adverse events an urgent issue.

PredicTox-KE advanced the science of adverse event prediction by creating computational models for prediction of drug toxicity and, in the longer term, predictions for personalization of therapy to mitigate adverse events.

In 2017, the American Society of Transplantation (AST) and American Society of Transplant Surgeons (ASTS) partnered with Critical Path Institute (C-Path) and other transplant community members to create the TTC. By facilitating a public-private partnership among scientists from the bio-pharmaceutical industry, diagnostics companies, academic institutions, professional societies, and government and regulatory agencies, TTC fostered consensus and data-driven research to speed the development of new immunosuppressive therapies (ISTs) for transplant recipients.

TTC  focus has primarily been seeking FDA qualification of the iBOX as a reasonably likely surrogate endpoint (RLSE) for long-term graft survival after kidney transplantation for use in pivotal clinical trials. In close collaboration with the Paris Transplant Group, TTC translated the iBOX from Loupy et al., 2019 into a regulatory endpoint for long-term graft survival.

In 2020, TTC received a Letter of Intent Determination from FDA that accepted iBOX into the Biomarker Qualification Program. TTC recognizes that the proposed RLSE cannot be used alone as a sole primary endpoint, and cannot replace efficacy failure. As such, TTC submitted the qualification plan, the second stage of the 3-stage process, to FDA this past July with a modified context of use to include coprimary with efficacy failure. This option does not compromise FDA’s current standard, and, in fact, would be held to higher standards than the current efficacy failure endpoint while providing sponsors a pathway to accelerated approval. In March 2024, TTC received the reviewability memo for the qualification plan submission. 

In December 2022, EMA granted biomarker qualification for iBOX, the first qualified endpoint ever in transplantation. iBox (with or without biopsy) is publicly available for use in kidney transplant clinical trials, giving sponsors the ability to demonstrate superiority of a novel IST compared to standard of care (SOC) at various time points post-transplant in pivotal or exploratory drug therapeutic studies. In Europe, this allowed labeling claims and promotion of superiority of the new therapy.   

TTC built a publicly available Sample size calculator using iBOX scores to assist with trial design. 

TTC developed an integrated international kidney transplant database comprised of about 23,000 kidney transplant recipients from 31 total cohorts and clinical trials to support TTC initiatives. Of these, 13,000 kidney transplant recipients from 16 transplant centers and five clinical trials are standardized to the Clinical Data Interchange Standards Consortium (CDISC).