Abstract 668: A recommender system to enhance drug development in oncology from phase I trials: The RESOLVED2 project
Authors: Beinse Guillaume , Tellier Virgile , Charvet Valentin , Borget Isabelle , Massard Christophe , Hollebecque Antoine , Verlingue Loic 
Published: August 2019

The industry and investigators’ communities are currently facing a conjunction of increasing number of anti-neoplastic agents (ANA) candidate for phase 1 clinical trials (P1CT) and an important attrition rate for final approval. Improvement of the drug development strategies in oncology could be achieved by machine learning supporting go/no-go decisions after phase I trial.

PubMed abstracts of P1CT reporting ANA were used together with pharmacological classification and molecular targets of ANA from DrugBank5.0 database. To account for heterogeneous follow-up we computed a new metric entitled Food & Drug Administration approval-free-survival (FDA-aFS). FDA-aFS is a right-censored variable defined by the time between first P1CT publication and the FDA approval date or censoring for compounds not approved up to 2018, July. A training set and a test set were randomly generated from the whole dataset with a 70%/30% ratio. A regression and a classification version of RESOLVED2 have been developed in the training set and evaluated independently on the test set. The RESOLVED2 regression model has been optimized using a Cox logistic regression on FDA-aFS, penalized by Lasso with cross validation on the training set. RESOLVED2 predictions have been compared to a model including common variables used for go/no-go decisions (toxicity, clinical activity, observation of complete tumor response). The RESOLVED2 classification model (predicted approved ANA vs predicted non-approved ANA) was computed in the training set using predicted probabilities derived from the regression model, by the identification of the cut-off reaching minimal log-rank derived p-value when comparing FDA-aFS of predicted approved versus predicted non-approved ANA.

We identified 462 ANA matching with DrugBank entries (P1CT publication interval: 1972-2017). Among 1411 variables, 28 remained in the model after Lasso penalization. The relation of the model to the FDA-aFS had a weighted concordance index of 0.89 on the independent test set, versus 0.69 for a classical model based on efficacy/toxicity. Classified approval predictions achieved a p&lt;10e-10 for association with observed FDA-aFS in the test set: at 6 years of follow-up, 73% (95%CI [49%; 86%]) of predicted approved drugs were indeed approved and 92% (95%CI [87%; 98%]) of predicted non-approved drugs were still not approved. A predicted approved drug was 16 times more likely to be approved compared to a predicted non-approved drug (Hazard ratio=16.4; 95%CI [8.40; 32.2]; p&lt;10e-10).

RESOLVED2 is an applicable machine learning model that could support early go/no-go decision as soon as P1CT completion based on accurate prediction of FDA approval for drugs in oncology.

Citation Format: Guillaume Beinse, Virgile Tellier, Valentin Charvet, Isabelle Borget, Christophe Massard, Antoine Hollebecque, Loic Verlingue. A recommender system to enhance drug development in oncology from phase I trials: The RESOLVED2 project [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 668.