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New Study Co-Authored by InsightRX Shows Model-Informed Precision Dosing Could Improve Efficacy of Hematopoietic Cell Transplants While Reducing Toxicity
Study shows therapeutic drug monitoring and a Bayesian dosing model co-developed by InsightRX achieves busulfan cAUC exposure goals with substantially more precision than conventional dosing approaches
SAN FRANCISCO, July 30, 2020 /PRNewswire/ -- Researchers at the University of California, San Francisco (UCSF) and healthcare technology company InsightRX demonstrated that a second-generation Bayesian pharmacokinetic (PK) model enabled highly accurate and repeatable target attainment for busulfan, a conditioning agent typically used in hematopoietic cell transplants (HCTs).
Busulfan is used in conditioning regimens before HCT for a variety of diseases including hematological malignancies, and increasingly in gene therapy. The drug has a narrow therapeutic index, meaning that dosing must be continually monitored and adjusted in order to maximize efficacy while reducing the likelihood of adverse drug events.
100% of patients dosed using the second-generation PK model with Bayesian forecasting achieved the target range of cumulative busulfan exposure (cAUC), as compared to 66% using the manufacturer's package insert and 88% using a first-generation PK model and non-compartmental analysis. The coefficient of variation, a measure of spread in target attainment, was 4.1% for patients dosed with the second-generation PK model, as compared to 14.8% and 17.1% for patients dosed using the package insert and first-generation model, respectively. This shows that the second-generation model provided a significant advantage over conventional dosing approaches to achieve target drug exposure.
"Accurately and precisely hitting busulfan dosing targets will reduce drug-related toxicity as well as increase the chance that HCT is successful in controlling a patient's disease," said Janel Long-Boyle, Associate Professor of Clinical Pharmacy at UCSF and principal investigator of the study. "InsightRX has been a great partner in developing these innovative methods that can ultimately save lives."
Clinicians and researchers at UCSF worked with InsightRX to develop the second-generation Bayesian PK model and implement it using InsightRX's cloud-based clinical decision support platform. The second-generation model takes into account individual patients' factors including age, body size and composition, and co-administration of other conditioning drugs, to provide individualized busulfan dosing regimens that improve clinical target attainment. The study, published this month in Frontiers in Pharmacology, a leading peer-reviewed pharmacology journal, details the development of the second-generation model and assessment of its accuracy and precision at achieving target busulfan exposure in children undergoing HCT, compared to conventional dosing methods. InsightRX co-founders Sirj Goswami and Ron Keizer co-authored the study.
"This milestone shows InsightRX's commitment to making meaningful scientific contributions that improve patient outcomes, as well as deploying these solutions to the point of care," said Sirj Goswami, InsightRX CEO and co-founder. "We look forward to helping InsightRX customers deliver superior patient care that can both save lives and cut costs."
InsightRX helps hospitals and health systems transition from one-size-fits-all drug dosing to individualized dosing at the point of care. With a cloud-based precision dosing platform, InsightRX leverages patient-specific data, quantitative pharmacology models, and machine learning to understand each patient's pharmacological profile to guide treatment decisions. InsightRX's end-to-end precision dosing solution enables health systems to predict and optimize dosing regimens, reach and maintain clinical targets, measure performance, and monitor clinical outcomes.
InsightRX recently announced a $10 million Series A funding.
InsightRX is a healthcare technology company that has developed a platform for precision medicine and clinical analytics designed to individualize treatment at the point of care. The platform leverages patient-specific data, pharmacology models, and machine learning to understand each patient's unique pharmacological profile to guide treatment decisions.