Patient-centric drug discovery using active learning (PeDAL™)
Improving the chances of successful clinical translation.
The current drug discovery process is long, costly, and suffers from high attrition rates due to poor translation of discoveries into the clinic. Much of this lack of success can be traced back to the early part of discovery which accounts for about one-third of the total cost and takes about 6 years.
Leveraging our core competence in profiling the drug response of patient tumors, our large knowledgebase of tumor drug response and other data, together with proven AI, Helomics has created a unique capability for oncology drug discovery that allows for the highly efficient screening of drug responses from thousands of diverse, well-characterized patient primary tumor cell lines.
This novel disruptive patient-centric approach is ideally suited to the early part of drug discovery (especially Hit-to-lead, lead optimization, and pre-clinical), resulting in better prioritization of compounds and better coverage of patient diversity. This will dramatically improve the chances of successfully translating discoveries into the clinic, resulting in lowered costs, shortened timelines, and most importantly enhanced “speed-to-patient” for new therapies.
Addressing the key challenges in oncology drug discovery.
- Exhaustive screening across thousands of heterogeneous tumors with existing technologies is cost and time prohibitive.
- Assessing a combination of drugs in the context of heterogeneity is even more prohibitive.
The key goal of early discovery is to deliver a drug candidate into clinical trials that is both effective and safe. Researchers often fall short of that goal with over 80% of compounds failing to translate into the clinic because they lack efficacy or have toxicity issues. For oncology discovery, many of these failures result from inadequately addressing the problem of tumor heterogeneity, both within and between tumors.
Real patient tissue testing typically occurs in late-stage development using expensive and time-consuming xenograft or other PDx platforms.
Our patient-centric drug discovery using active learning (PeDAL) platform efficiently screens compounds over thousands of highly characterized patient tumor primary cell lines.
- Diversity of drug responses across a set of patients
- Correlations and comparisons with existing standard-of-care drugs
- Model of different drug responses for patients with different tumor properties
PeDAL is a unique technology that combines a proprietary, clinically validated patient tumor cell line assay, a vast knowledgebase of proprietary and public data, and Active Learning – the Active Learning allows for the efficient exploration of compound drug responses against a large diverse patient “space”. PeDAL offers researchers the opportunity to bring patient diversity efficiently and cost-effectively into drug discovery much earlier.
How does PeDAL work?
PeDAL works by iterative cycles of active-learning powered Learn-Predict-Test (L-P-T) to guide the testing of patient-specific compound responses using the TruTumor assay and patient cell lines to build a comprehensive predictive model of patient responses to compounds. This predictive model can then be used to rank compounds by the fraction of patients of certain profiles that respond as well as the set of compounds that provide the best coverage across patients.
What does PeDAL deliver?
PeDAL’s unique patient and tumor-centric AI-driven approach can rapidly and cost-effectively screen hundreds of compounds in thousands of tumor cell lines, and gain valuable information about off-target effects and delivers:
- A ranked list of drug candidates by responsiveness
- Sets of drug candidates that provide maximum patient coverage
- Biomarker profiles of patients that respond to specific drug candidates
PeDAL can deliver drug candidates targeted at a specific patient profile as early as the hit-to-lead stage of discovery, significantly increasing the chance of clinical success, leading to a dramatic improvement in both the success, time, and cost of your oncology discovery programs.