We have applied computational analysis to identify clinical trials expected to be of value for personalisation of treatment. From an initial set of ~2000 candidate trials, this procedure resulted in the collection/preprocessing of 45 project-relevant datasets involving 18 unique therapies.
Analysis of these datasets shows:
- accurate patient-level therapy-response models can be learned from aggregate datasets (usually only aggregate data is posted in clinical trial reports and other publicly-available databases);
- it produced 22 ‘core’ models with which to predict patient response to 15 important MS therapies and facilitate personalized treatment optimization;
- identified predictive features/biomarkers for each model, some of which appear novel and almost all of which are measurable in the course of routine MS care.
Predictive modeling yielded a number of interesting findings, including:
- detection of unsuccessful clinical trials which could have been successful had proper cohort selection been performed (e.g. CONCERTO laquinimod trial); crucially, predictive patient selection can be achieved using data available before the trial is initiated;
- demonstration that personalized therapy optimization models can be learned for established first-line and second-line treatments (e.g. interferon b, glatiramer acetate, fingolimod, natalizumab, mitoxantrone) as well as for less commonly-used drugs;
- derivation of responder/non-responder prediction models for the five therapies listed above and also for fampridine (based on clinical biomarkers), dimethyl fumarate (blood-borne biomarkers), intrathecal corticosteroid therapy (CSF/clinical data), rituximab (MRI data), injectables (clinical data), apheresis therapy (brain biopsy data), laquinimod (clinical/MRI data), and teriflunomide (early clinical response).
Volv performed analysis across the studies highlighted above and it produced 22 ‘core’ models with which to predict patient response to 15 important MS therapies and facilitate personalized treatment optimization. In the example below we focus on one of those core models.
- Beyond this we then performed further analysis to provide concise summaries of:
- ‘new concepts’ identified via computational analysis of clinical trials (e.g. promising disease-modifying therapies (DMTs), novel biomarkers for responder/non-responder prediction);
- simplified/accessible versions of prediction models.
We identified ten ‘core’ sets of models and their main purposes (e.g. optimal therapy selection, assessment of unapproved treatments), and for each core model set, we then specified type/output/predictive features for all component models and, where feasible, derive simplified versions of the models using our clinically interpretable model building from inTrigue.