Oncology clinical trials represent a significant investment in the health industry. The costs of such trials can vary widely based on factors such as the number of patients, the type of intervention, and the location of the trial. According to a report by JAMA Internal Medicine, the median cost of a clinical trial between 2015 and 2016 was $19 million, but costs can easily range up to 100 times that. More specifically, for oncology trials, an average per-patient cost was found to be $59,500 in a report by the Pharmaceutical Research and Manufacturers of America (PhRMA).
As these costs continue to rise, the ability to predict and manage them becomes increasingly vital. To that end, the use of machine learning in estimating clinical trial costs has proven to be a valuable tool. One such tool is the Clinical Trial Risk Tool developed by Fast Data Science.
Fast Data Science’s Clinical Trial Risk Tool uses machine learning models to analyse clinical trial protocols, predict the cost of a trial, and identify the possible risks. The prices of each intervention in the protocol, including gene and cell therapy are considered, as well as the number of interventions all play a part in this prediction process. The tool successfully predicts the cost on a per-patient basis.
| indication (longer) | Technology | CT.gov URL | Enrollment | Trial Phase | Total Cost | Per Patient Cost ($PP) |
|---|---|---|---|---|---|---|
| Advanced Myeloid Malignancy | biologic drug | 30 | Phase 1 | 328000 | 10933.3 | |
| Blood Cancer | biologic drug | NCT03483324 | 9 | Phase 1 | 5000000 | 555556 |
| Blood Cancer | biologic drug | NCT03925935 | 24 | Phase 1 | 6192579 | 258024 |
| B cell cancers, Leukemia | biologic drug | NCT03088878 | 156 | Phase 1/2 | 18292674 | 117261 |
| Blood Cancer | biologic drug | NCT02222688 | 26 | Phase 1 | 4179598 | 160754 |
| Colon Cancer | biologic drug | NCT02953782 | 112 | Phase 1/2 | 10234048 | 91375.4 |
| Leukemia, Acute Myeloid (AML) | biologic drug | NCT03248479 | 96 | Phase 1 | 5000000 | 52083.3 |
| Blood Cancer, Solid Tumors | biologic drug | NCT02216409 | 88 | Phase 1 | 6505568 | 73926.9 |
| Breast Cancer | biologic drug | NCT00781612&draw=2&rank=1 | 720 | Phase 3 | - | 104186 |
| Stage IV Melanoma | cell therapy | NCT00438984 | 11 | Phase 1 | 936164 | 85105.8 |
| Stage IV Breast Cancer | cell therapy | NCT00791037 | 23 | Phase 1/2 | 2236359 | 97233 |
| Non-Small Cell Lung Cancer | cell therapy | NCT00850785 | 6 | Phase 1 | 653850 | 108975 |
| Brain Cancer | cell therapy | NCT02546102 | 414 | Phase 3 | 5391016 | 13021.8 |
| Leukemia, Acute Myeloid (AML) | cell therapy | NCT03301597 | 146 | Phase 2 | 4310000 | 29520.5 |
| Melanoma | cell therapy | NCT01875653 | 4 | Phase 3 | 3000000 | 750000 |
| Blood Cancer, Bone Marrow Transplant and Viral Infection | cell therapy | NCT03475212 | 60 | Phase 1/2 | 4825587 | 80426.4 |
| Brain Cancer | cell therapy | NCT02208362 | 92 | Phase 1 | 12753854 | 138629 |
| Brain Cancer, Breast Cancer | cell therapy | NCT03696030 | 39 | Phase 1 | 9015149 | 231158 |
| Multiple Myeloma | cell therapy | NCT03288493 | 180 | Phase 1 | 19813407 | 110074 |
| B cell cancers, Leukemia | cell therapy | NCT03233854 | 57 | Phase 1 | 11034982 | 193596 |
| Lung Cancer | cell therapy | NCT03546361 | 36 | Phase 1 | 11815315 | 328203 |
| Melanoma, Skin cancer | cell therapy | NCT03240861 | 12 | Phase 1 | 14144221 | 1.17869e+06 |
| Sarcoma | cell therapy | NCT03240861 | 12 | Phase 1 | 4693839 | 391153 |
| HIV-related Lymphoma, HIV/AIDS | gene therapy | NCT02797470 | 18 | Phase 1/2 | 8414265 | 467459 |
| Prostate cancer | small molecule drug | 232 | Phase 2/3 | 2969523 | 12799.7 | |
| Acute Myeloid Leukemia | small molecule drug | 60 | Phase 1/2 | 1166746 | 19445.8 | |
| Non-Small Cell Lung Cancer | small molecule drug | 140 | Phase 2 | 5852288 | 41802.1 | |
| Solid Tumors | small molecule drug | NCT01954316 | 48 | Phase 1 | 5683693 | 118410 |
This is a valuable resource for the health industry, as it helps to identify and quantify the potential risks associated with a specific trial and maneuver through the budgeting process more confidently.
One of the big advantages of using machine learning in cost prediction is it provides a means to analyze complex and multifaceted data, and learn patterns or trends, much faster than humans. This can significantly reduce the time it takes to estimate the cost of clinical trials.
The costs associated with clinical trials are immense, but with the aid of advanced technology and machine learning, researchers can get an accurate estimate of a trial’s cost from the protocol. Despite the inherent difficulties in predicting such complex costs, tools like Fast Data Science’s Clinical Trial Risk Tool provide a promising option to navigate this challenging landscape.
Estimating costs accurately can be highly beneficial to efficiently utilize resources, paving the way for better trial designs and ultimately fostering an environment that supports the rapid advancement of new treatments.
