Data_Sheet_1_Applications for open access normalized synthesis in metastatic prostate cancer trials.docx

Recent metastatic castration-resistant prostate cancer (mCRPC) clinical trials have integrated homologous recombination and DNA repair deficiency (HRD/DRD) biomarkers into eligibility criteria and secondary objectives. These trials led to the approval of some PARP inhibitors for mCRPC with HRD/DRD indications. Unfortunately, biomarker-trial outcome data is only discovered by reviewing publications, a process that is error-prone, time-consuming, and laborious. While prostate cancer researchers have written systematic evidence reviews (SERs) on this topic, given the time involved from the last search to publication, an SER is often outdated even before publication. The difficulty in reusing previous review data has resulted in multiple reviews of the same trials. Thus, it will be useful to create a normalized evidence base from recently published/presented biomarker-trial outcome data that one can quickly update. We present a new approach to semi-automating normalized, open-access data tables from published clinical trials of metastatic prostate cancer using a data curation and SER platform. Clinicaltrials.gov and Pubmed.gov were used to collect mCRPC clinical trial publications with HRD/DRD biomarkers. We extracted data from 13 publications covering ten trials that started before 22nd Apr 2021. We extracted 585 hazard ratios, response rates, duration metrics, and 543 adverse events. Across 334 patients, we also extracted 8,180 patient-level survival and biomarker values. Data tables were populated with survival metrics, raw patient data, eligibility criteria, adverse events, and timelines. A repeated strong association between HRD and improved PARP inhibitor response was observed. Several use cases for the extracted data are demonstrated via analyses of trial methods, comparison of treatment hazard ratios, and association of treatments with adverse events. Machine learning models are also built on combined and normalized patient data to demonstrate automated discovery of therapy/biomarker relationships. Overall, we demonstrate the value of systematically extracted and normalized data. We have also made our code open-source with simple instructions on updating the analyses as new data becomes available, which anyone can use even with limited programming knowledge. Finally, while we present a novel method of SER for mCRPC trials, one can also implement such semi-automated methods in other clinical trial domains to advance precision medicine.</p

Table_1_Factors Affecting Combination Trial Success (FACTS): Investigator Survey Results on Early-Phase Combination Trials.DOCX

Experimental therapeutic oncology agents are often combined to circumvent tumor resistance to individual agents. However, most combination trials fail to demonstrate sufficient safety and efficacy to advance to a later phase. This study collected survey data on phase 1 combination therapy trials identified from ClinicalTrials.gov between January 1, 2003 and November 30, 2017 to assess trial design and the progress of combinations toward regulatory approval. Online surveys (N = 289, 23 questions total) were emailed to Principal Investigators (PIs) of early-phase National Cancer Institute and/or industry trials; 263 emails (91%) were received and 113 surveys completed (43%). Among phase 1 combination trials, 24.9% (95%CI: 15.3%, 34.4%) progressed to phase 2 or further; 18.7% (95%CI: 5.90%, 31.4%) progressed to phase 3 or regulatory approval; and 12.4% (95%CI: 0.00%, 25.5%) achieved regulatory approval. Observations of “clinical promise” in phase 1 combination studies were associated with higher rates of advancement past each milestone toward regulatory approval (cumulative OR = 11.9; p = 0.0002). Phase 1 combination study designs were concordant with Clinical Trial Design Task Force (CTD-TF) Recommendations 79.6% of the time (95%CI: 72.2%, 87.1%). Most discordances occurred where no plausible pharmacokinetic or pharmacodynamic interactions were expected. Investigator-defined “clinical promise” of a combination is associated with progress toward regulatory approval. Although concordance between study designs of phase 1 combination trials and CTD-TF Recommendations was relatively high, it may be beneficial to raise awareness about the best study design to use when no plausible pharmacokinetic or pharmacodynamic interactions are expected.</p

S2: Methods for SOD2 Genotyping and Validation from Muscadine Grape Skin Extract (MPX) in Men with Biochemically Recurrent Prostate Cancer: A Randomized, Multicenter, Placebo-Controlled Clinical Trial

S2 provides details on the methods used to determine and validate SOD2 germline genotypes.</p

S3. Methods for ellagic acid, urolithin A, quercetin, and resveratrol pharmacokinetics from Muscadine Grape Skin Extract (MPX) in Men with Biochemically Recurrent Prostate Cancer: A Randomized, Multicenter, Placebo-Controlled Clinical Trial

S3 provides detailed descriptions of methods used in pharmacokinetic assays.</p

S4. Supplementary Results: Prior ADT Treatment from Muscadine Grape Skin Extract (MPX) in Men with Biochemically Recurrent Prostate Cancer: A Randomized, Multicenter, Placebo-Controlled Clinical Trial

Section S4 shows increased lengthening in PSADT for patients with SOD2 Ala/Ala genotype were seen in ADT naive patients.</p

S1: MPX Manufacturing Process and Chemical Constituents from Muscadine Grape Skin Extract (MPX) in Men with Biochemically Recurrent Prostate Cancer: A Randomized, Multicenter, Placebo-Controlled Clinical Trial

S1 describes the process employed in manufacturing MPX and summarizes the chemical constituents of the MPX batch used in this study.</p

Supplementary Figure S4.1 from Muscadine Grape Skin Extract (MPX) in Men with Biochemically Recurrent Prostate Cancer: A Randomized, Multicenter, Placebo-Controlled Clinical Trial

Supplementary Figure S4.1 PSADT Change from Baseline in Months by MPX Treatment Group and Prior ADT Treatment</p