Analytics plays a big role in modeling clinical trials and predictive analytics is one such technique that has been embraced by clinical researchers. Machine learning algorithms can be applied at various stages in the drug discovery process – from early compound selection to clinical trial simulation. Data scientists have been applying machine learning algorithms to clinical trial data in order to identify predictive patterns and correlations between clinical outcomes, patient demographics, drug response phenotypes, medical history, and genetic information. Predictive analytics has the potential to enhance clinical research by helping accelerate clinical trials through predictive modeling of clinical outcome probability for better treatment decisions with reduced clinical trial costs. In this blog post, you will learn about different clinical trials predictive analytics usecases.
Clinical trial analytics form a key part of clinical research. Clinical research includes the development of disease-relevant, preclinical models of disease has undoubtedly been one of the most significant changes, as well as the move from a one-drug-fits-all strategy to precision medicine’s effectiveness. Since the completion of the human genome, our sector has evolved to adopt data-driven methods, as data has become a critical component. AI is enabling new insights such as protein structure prediction with innovations like Google’s DeepMind AlphaFold. The preclinical models can be used to explore the mechanism of action, clinical trial simulations that are much more personalized in order to enhance clinical research.
What are clinical trials and how are they conducted?
Clinical trials are the procedure of disease research that helps pharmaceutical companies learn more about how drugs function and which side effects they might cause before beginning human testing. In clinical trials, doctors and scientists test new medicines in people in order to see if they are safe and effective. A clinical trial may use a combination of standard medical approaches (such as surgery or medication) or special methods developed for research purposes (for example, gene therapy). A clinical trial is conducted as a group of clinical studies that are designed to answer specific questions about the safety or effectiveness of an investigational drug. Clinical trials may be conducted in three phases:
- A clinical trial is started only after preliminary laboratory and animal testing has been completed, and the study design (including treatment groups, number of patients) has been approved by a research ethics committee (institutional review board) and clinical research ethics committee.
- In the next phase, clinical trials are conducted in a small number of people, usually 20 or fewer. These clinical trials are designed to test the drug’s effectiveness and how well it is tolerated by patients.
- In the final phase, clinical trials are conducted in more people to confirm the drug’s effectiveness, monitor side effects, and collect information that will allow the clinical trial sponsor or investigator to determine whether the medicine should be approved by regulatory authorities for sale.
What are some predictive analytics use cases in clinical trials?
Before getting into predictive analytics use cases for clinical trials, let’s get some information around clinical trial data. Clinical trial data are increasingly available through public registries (e.g., ClinicalTrials.gov). Meanwhile, real-world data, such as electronic health records (EHRs), claims, and billing data, are increasingly being used in drug development. Combining trial designs (e.g., different eligibility criteria) with the real-world data could provide insights on the impact of study design in the real-world patient population and hence predict its generalizability post-approval of the drug.
Predictive modeling is a widely used clinical trials application of predictive analytics that can be applied to extract useful information from clinical trial datasets, trends, and associations in large clinical trial datasets with many variables for better decision making – ultimately leading to more accurate clinical research results. Clinical researchers use predictive models based on machine learning algorithms to predict the outcome of the clinical trial of clinical research.
Predictive analytics are being applied in clinical research to improve the success rate of clinical studies. Machine learning algorithms may assist clinical trial researchers in some of the following use cases:
- Predict clinical trial outcomes: Predict which patients will respond favorably or poorly to a treatment based on their genetic make-up, age, medical history, and other information. Clinical research analysts may also use predictive analytics to detect adverse events during clinical trials by analyzing real-world evidence sources such as EHRs and claims data, in addition to clinical studies. This can be done through predictive analytics models that examine potential clinical events that could affect or influence clinical trials, such as hospitalization or death.
- Predict side effects to medications: Clinical trial researchers can use clinical research data to predict which patients will be most likely to experience certain side effects.
- Drug-drug interaction prediction: Assessing interactions between drugs used to treat different diseases or disorders. Predictive/machine learning modeling can be used to extract insights into the adverse events that could occur when two or more drugs are given together. It can also help to identify lower-risk interactions through analysis of available clinical data and in silico clinical studies.
- Predict clinical trial enrollment: Utilizing clinical data, machine learning modeling techniques can be used to predict the patient groups (responders) that are most likely to enroll in clinical trials. There are different machine learning techniques that can be used to predict appropriate patient groups. One of those techniques is extracting clinical information (NLP techniques) from patient health records and applying clustering algorithms to group the probable patient groups which can take part in clinical trials. This information could then be utilized by clinical trial sponsors and investigators to better focus their marketing efforts on those groups of potential study participants who may have a higher rate of clinical trial participation.
- Predict clinical trial dropout rates (nonresponders): Predictive/machine learning modeling can be used to predict clinical study completion, or how likely it is for a clinical participant to complete the full course of treatment. This information could be used by clinical researchers and sponsors in their interpretation of clinical data, as well as during patient recruitment
Predictive analytics is a clinical research tool that can be used to improve the success rate of clinical trials. Machine learning algorithms may assist clinical trial researchers in some of the following use cases: predicting clinical trial outcomes, predicting side effects to medications, drug-drug interaction prediction, and predict clinical trial enrollment. For example, predictive analytics models could detect adverse events during clinical trials by analyzing real-world evidence sources such as EHRs and claims data in addition to clinical studies. This can be done through predictive analytics models that examine potential clinical events that could affect or influence clinical trials for example hospitalization or death. Clinical research analysts may also use predictive analytics to predict which patients will respond favorably or poorly to a treatment based on their genetic make-up age medical history and other information. In addition, clinical trial researchers can use clinical research data to predict which patients will be most likely to experience certain side effects or clinical study completion, or how likely it is for a clinical participant to complete the full course of treatment. This information could then be utilized by clinical trial sponsors and investigators to better focus their marketing efforts on those groups of potential study participants who may have a higher rate of clinical trial participation. If you would like to know greater details, please reach out to us.