1
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Prediction of Drug Approval After Phase I Clinical Trials in Oncology: RESOLVED2

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          PURPOSE

          Drug development in oncology currently is facing a conjunction of an increasing number of antineoplastic agents (ANAs) candidate for phase I clinical trials (P1CTs) and an important attrition rate for final approval. We aimed to develop a machine learning algorithm (RESOLVED2) to predict drug development outcome, which could support early go/no-go decisions after P1CTs by better selection of drugs suitable for further development.

          METHODS

          PubMed abstracts of P1CTs reporting on ANAs were used together with pharmacologic data from the DrugBank5.0 database to model time to US Food and Drug Administration (FDA) approval (FDA approval-free survival) since the first P1CT publication. The RESOLVED2 model was trained with machine learning methods. Its performance was evaluated on an independent test set with weighted concordance index (IPCW).

          RESULTS

          We identified 462 ANAs from PubMed that matched with DrugBank5.0 (P1CT publication dates 1972 to 2017). Among 1,411 variables, 28 were used by RESOLVED2 to model the FDA approval-free survival, with an IPCW of 0.89 on the independent test set. RESOLVED2 outperformed a model that was based on efficacy/toxicity (IPCW, 0.69). In the test set at 6 years of follow-up, 73% (95% CI, 49% to 86%) of drugs predicted to be approved were approved, whereas 92% (95% CI, 87% to 98%) of drugs predicted to be nonapproved were still not approved (log-rank P < .001). A predicted approved drug was 16 times more likely to be approved than a predicted nonapproved drug (hazard ratio, 16.4; 95% CI, 8.40 to 32.2).

          CONCLUSION

          As soon as P1CT completion, RESOLVED2 can predict accurately the time to FDA approval. We provide the proof of concept that drug development outcome can be predicted by machine learning strategies.

          Related collections

          Most cited references15

          • Record: found
          • Abstract: not found
          • Article: not found

          Making the first move in EGFR-driven or ALK-driven NSCLC: first-generation or next-generation TKI?

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Cisplatin and fluorouracil with or without panitumumab in patients with recurrent or metastatic squamous-cell carcinoma of the head and neck (SPECTRUM): an open-label phase 3 randomised trial.

            Previous trials have shown that anti-EGFR monoclonal antibodies can improve clinical outcomes of patients with recurrent or metastatic squamous-cell carcinoma of the head and neck (SCCHN). We assessed the efficacy and safety of panitumumab combined with cisplatin and fluorouracil as first-line treatment for these patients. This open-label phase 3 randomised trial was done at 126 sites in 26 countries. Eligible patients were aged at least 18 years; had histologically or cytologically confirmed SCCHN; had distant metastatic or locoregionally recurrent disease, or both, that was deemed to be incurable by surgery or radiotherapy; had an Eastern Cooperative Oncology Group performance status of 1 or less; and had adequate haematological, renal, hepatic, and cardiac function. Patients were randomly assigned according to a computer-generated randomisation sequence (1:1; stratified by previous treatment, primary tumour site, and performance status) to one of two groups. Patients in both groups received up to six 3-week cycles of intravenous cisplatin (100 mg/m(2) on day 1 of each cycle) and fluorouracil (1000 mg/m(2) on days 1-4 of each cycle); those in the experimental group also received intravenous panitumumab (9 mg/kg on day 1 of each cycle). Patients in the experimental group could choose to continue maintenance panitumumab every 3 weeks. The primary endpoint was overall survival and was analysed by intention to treat. In a prospectively defined retrospective analysis, we assessed tumour human papillomavirus (HPV) status as a potential predictive biomarker of outcomes with a validated p16-INK4A (henceforth, p16) immunohistochemical assay. Patients and investigators were aware of group assignment; study statisticians were masked until primary analysis; and the central laboratory assessing p16 status was masked to identification of patients and treatment. This trial is registered with ClinicalTrials.gov, number NCT00460265. Between May 15, 2007, and March 10, 2009, we randomly assigned 657 patients: 327 to the panitumumab group and 330 to the control group. Median overall survival was 11·1 months (95% CI 9·8-12·2) in the panitumumab group and 9·0 months (8·1-11·2) in the control group (hazard ratio [HR] 0·873, 95% CI 0·729-1·046; p=0·1403). Median progression-free survival was 5·8 months (95% CI 5·6-6·6) in the panitumumab group and 4·6 months (4·1-5·4) in the control group (HR 0·780, 95% CI 0·659-0·922; p=0·0036). Several grade 3 or 4 adverse events were more frequent in the panitumumab group than in the control group: skin or eye toxicity (62 [19%] of 325 included in safety analyses vs six [2%] of 325), diarrhoea (15 [5%] vs four [1%]), hypomagnesaemia (40 [12%] vs 12 [4%]), hypokalaemia (33 [10%] vs 23 [7%]), and dehydration (16 [5%] vs seven [2%]). Treatment-related deaths occurred in 14 patients (4%) in the panitumumab group and eight (2%) in the control group. Five (2%) of the fatal adverse events in the panitumumab group were attributed to the experimental agent. We had appropriate samples to assess p16 status for 443 (67%) patients, of whom 99 (22%) were p16 positive. Median overall survival in patients with p16-negative tumours was longer in the panitumumab group than in the control group (11·7 months [95% CI 9·7-13·7] vs 8·6 months [6·9-11·1]; HR 0·73 [95% CI 0·58-0·93]; p=0·0115), but this difference was not shown for p16-positive patients (11·0 months [7·3-12·9] vs 12·6 months [7·7-17·4]; 1·00 [0·62-1·61]; p=0·998). In the control group, p16-positive patients had numerically, but not statistically, longer overall survival than did p16-negative patients (HR 0·70 [95% CI 0·47-1·04]). Although the addition of panitumumab to chemotherapy did not improve overall survival in an unselected population of patients with recurrent or metastatic SCCHN, it improved progression-free survival and had an acceptable toxicity profile. p16 status could be a prognostic and predictive marker in patients treated with panitumumab and chemotherapy. Prospective assessment will be necessary to validate our biomarker findings. Amgen Inc. Copyright © 2013 Elsevier Ltd. All rights reserved.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Cancer Drug Response Profile scan (CDRscan): A Deep Learning Model That Predicts Drug Effectiveness from Cancer Genomic Signature

              In the era of precision medicine, cancer therapy can be tailored to an individual patient based on the genomic profile of a tumour. Despite the ever-increasing abundance of cancer genomic data, linking mutation profiles to drug efficacy remains a challenge. Herein, we report Cancer Drug Response profile scan (CDRscan) a novel deep learning model that predicts anticancer drug responsiveness based on a large-scale drug screening assay data encompassing genomic profiles of 787 human cancer cell lines and structural profiles of 244 drugs. CDRscan employs a two-step convolution architecture, where the genomic mutational fingerprints of cell lines and the molecular fingerprints of drugs are processed individually, then merged by ‘virtual docking’, an in silico modelling of drug treatment. Analysis of the goodness-of-fit between observed and predicted drug response revealed a high prediction accuracy of CDRscan (R2 > 0.84; AUROC > 0.98). We applied CDRscan to 1,487 approved drugs and identified 14 oncology and 23 non-oncology drugs having new potential cancer indications. This, to our knowledge, is the first-time application of a deep learning model in predicting the feasibility of drug repurposing. By further clinical validation, CDRscan is expected to allow selection of the most effective anticancer drugs for the genomic profile of the individual patient.
                Bookmark

                Author and article information

                Journal
                JCO Clinical Cancer Informatics
                JCO Clinical Cancer Informatics
                American Society of Clinical Oncology (ASCO)
                2473-4276
                November 2019
                November 2019
                : 3
                : 1-10
                Affiliations
                [1 ]Gustave Roussy Cancer Campus, Villejuif, France
                [2 ]Université Paris-Saclay, Le Kremlin-Bicêtre, France
                [3 ]Université Paris-Saclay, Villejuif, France
                [4 ]Université Versailles Saint-Quentin-en-Yvelines, Villejuif, France
                [5 ]Université Paris-Sud, Paris, France
                Article
                10.1200/CCI.19.00023
                9ffbc922-9160-4fa7-bbaa-7178d9a22683
                © 2019
                History

                Comments

                Comment on this article