Clinical trials are essential for developing new treatments, discovering new ways to diagnose diseases, and minimizing drug side effects. So far, we have discussed unconventional trial designs, challenges and promises of DCTs in Oncology, employment of digital endpoints in clinical trials, and replication/reproducibility crisis in preclinical studies.
The current article explains how predictive analysis applications play an integral role in healthcare, shaping the future of clinical trials. Employing predictive analysis in clinical trials can bring to light heaps of information related to medication, disease progression, associated side effects, efficacy, etc., and help researchers overcome common pain points and barriers.
3 + 1 reasons to incorporate predictive analytics in your clinical trials
While designing and conducting clinical trials, certain things can go wrong. Clinical trials are complicated and challenging, from finding participants with a suitable clinical profile and persuading them to enroll to managing the risk of drug side effects and facing early termination and low success rates. The following paragraphs describe how predictive analysis can enhance clinical trials and unlock their potential, paving the road for precision medicine.
- Easier, faster, and targeted Clinical Trial Enrollment
Finding the right patients to enroll in the trials is difficult and requires meticulous planning. Utilizing access to patients’ healthcare records, predictive analytic models can compare the patients’ medical profiles to ongoing medical trials and predict a pool of patients who might be eligible for similar upcoming studies, assisting in cutting down the enrollment time.
e-DICT helps you meet your clinical trial recruitment goals
e-DICT is a Machine Learning tool that helps save time and money in patient recruitment and increases patient diversity while monitoring in real-time progress of all recruitment stages. By comparing a trial’s characteristics with the patient database, e-DICT finds qualified individuals, even from underrepresented communities, who would be a good fit for the trial. Once the potential participants are notified about the trial, they can decide whether they wish to participate, and their engagement from enrollment till completion is tracked.
- Reduced drug side effects. Fewer undesired interactions with other medications.
Predictive modeling in clinical research can generate insights about a patient’s tolerability for a particular drug and reduce cases of adverse drug reactions. Scientists use machine learning to study research data to predict which patients can experience side effects from certain drugs. Moreover, machine learning can identify adverse reactions when two or more drugs are administered together, assessing the level of their interaction (lower-risk vs. high-risk interaction scenarios).
Novartis Institutes for BioMedical Research and Harvard Medical School join forces for safer drugs.
In 2020, a team of researchers from Novartis Institutes for BioMedical Research and Harvard Medical School created an ML tool that could identify proteins associated with drug side effects. The algorithm results indicate which proteins could represent drug targets contributing to certain side effects.
As more and new data is fed into the algorithm, the program can help determine which drug might cause a certain side effect on its own or when administered in combination with other medicines. This knowledge can benefit human clinical trials as it can reduce the risk of side effects.
- Improved clinical trials outcome and higher success rate
Predictive analytics and machine learning can also predict how and what patient profile will respond favorably to treatment based on a patient’s clinical history, lifestyle, age, genetic profile, etc. Such insights could further provide effective patient treatment and reduce events leading to hospitalization or death.
Besides this, predictive analytics uncover the main reasons that lead to the termination of clinical trials and predict which trials could result in early termination, allowing for better planning, resource allocation, and cost control, and a higher success rate.
- Contribution to personalized medicine development
Understanding how patients react to medications contributes further to crafting personalized medicines. Acumens collected during a clinical trial on how a drug works for a particular patient. Studying dosing patterns, patient tolerability, digital biomarkers, and adherence makes it possible to customize medications at an individual’s level.
Τhings to consider before employing predictive analytics in your clinical trials
Employing predictive analytics for incorrect health data can be risky. Even though medical data has been digitized and is available in the form of EHR, concerns about their authenticity and completeness still need to be addressed.
Some of the key problems researchers face with medical data records are:
- Incomplete information capture
- Patients deliberately not sharing correct and complete medical history
- Communication errors during care transition
- Unavailability of the latest medical conditions in medical records
The main takeaway when employing predictive analysis and machine learning across multiple aspects of a clinical trial, is that we should always consider and address the challenges associated with inconsistencies in healthcare data to get accurate results.
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Sources:
https://www.nature.com/articles/s41598-021-82840-x
https://hms.harvard.edu/news/predicting-side-effects
https://www.acclinate.com/for-pharmaceutical-companies
https://vitalflux.com/clinical-trials-predictive-analytics-use-cases/
https://webmedy.com/blog/the-importance-of-predictive-modeling-for-clinical-trials/