Solutions

bfLEAP™ discovers patient-specific variables and clinical outcomes

Patients with schizophrenia are highly likely to discontinue treatment with their prescribed antipsychotic, due largely to lack of efficacy and intolerable side effects. 

This is evidenced by data from various clinical studies, including the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) – the largest antipsychotics trial ever conducted. The CATIE data showed that 74% of patients discontinued before 18 months. BullFrog analyzed publicly available (de- identified) data from CATIE*, including novel pharmacokinetic and genetic data from these patients, to identify correlative relationships between individual genes and poor patient outcomes. 

Most new therapeutics will fail at some point in preclinical or clinical development. This is the primary driver of the high cost of developing new therapeutics. A major part of the difficulty in developing new therapeutics is efficient integration of complex and high dimensional data generated at each stage of development to de-risk subsequent stages of the development process. Artificial Intelligence and Machine Learning (AI/ML) has emerged as a digital solution to help solve this problem. Most current AI/ML platforms still fall short in their ability to synthesize disparate, high-dimensional data for actionable insight. BullFrog AI’s bfLEAP™ analytical AI/ML platform, however, is able to surmount the challenges of scalability and flexibility currently hindering researchers and clinicians from having a more precise, multi-dimensional understanding of their data. As such, BullFrog AI is deploying bfLEAP™ for use at several critical stages of development with the intention of streamlining data analytics in therapeutics development, decreasing the overall development costs by decreasing failure rates for new therapeutics, and impacting the lives of countless patients that may have otherwise not received the therapies they need. 

bfLEAP™ identifies novel covariates for precise patient stratification

Heart failure (HF) affects ~26 million people globally. For this analysis – the client was an international company developing a new device for monitoring of CVD progression in adult patients. The client’s device offers a fast, accurate, and non-invasive measurement of patient CV based on several underlying physiological inputs. Over time, it proved difficult for client to capture the highest-value evidence to meet its goals. The client enlisted BullFrog AI to utilize bfLEAP™ to analyze their ongoing clinical trial data, to improve their ongoing CVD clinical trial by: a) identifying niche patient populations of highest value, b) connections between outcomes of interest and client device readout, and c) other actionable insights toward capturing highest-value data for the ongoing AHF trial. 

bfLEAP™ identifies of novel genetic biomarkers predicting patient response to antipsychotic treatment

BullFrog analyzed de-identified data from CATIE, including novel pharmacokinetic and genetic data from these patients, to identify correlative relationships between patient data (genetic, demographic, clinical, etc.) and poor patient outcomes. Using bfLEAP™ 2.0 system, BullFrog AI successfully identified a multi-dimensional cluster of key correlates with poor schizophrenia outcomes in response to the anti-psychotic drug olanzapine.