Praedicare Leverages AI, Mathematical Models of Disease Progression and Mapping in World’s First In Silico Clinical Trial of Its Kind

DALLAS, Texas — (NEWSWISE)

Praedicare’s pioneering system establishes new precedents of efficacy, safety, and speed by predicting a new drug treatment for the most common cause of non-tuberculous mycobacterial lung disease.

The in silico clinical trial demonstrated, without risk of patients’ lives:

• 2X the efficacy of the current treatment (>80% vs 39%)
• 3X shorter treatment time to cure (6 vs 18 months)
• 1 drug compared to a 3-drug combo for the standard of care
• Preclinical results in shorter time than animal models

Praedicare announced today they have conducted the world’s first in silico clinical trial of a new treatment using proprietary mathametical and AI model systems combined with their hollow fiber wet lab systems. In a study published in The Journal of Infectious Diseases, Praedicare explored a drug candidate to treat Mycobacterium avium complex (MAC), the leading cause of non-tuberculous mycobacterial lung disease. The hollow fiber system model for mycobacteria (HFS), developed by Praedicare’s Dr. Tawanda Gumbo, and approved by regulators for diseases such as tuberculosis, is an alternative to animal testing. In their groundbreaking virtual clinical trial, Praedicare researchers compared the standard of care for MAC to a simpler, 6-month single-drug treatment with a single commonly available antibiotic. Using their hollow fiber model lung instead of mouse models, combined with their proprietary mathematical mapping and AI systems representing human populations, the standard of care treatment was shown to accurately reproduce patient sputum culture conversion, or clearance of the disease, as currently seen in the clinic. This new treatment candidate, ceftriaxone, was then predicted to achive that faster in a greater proportion of patients. Ceftriaxone can now proceed to real patient trials with higher confidence than preclinical animal testing.

MAC lung disease is a growing public health concern, with a 5-year mortality rate of 25% and global studies reporting its prevalence doubling over recent years. The current standard of care, a grueling 1.5 years-long, triple-antibiotic regimen, achieves only a 43% cure rate and sees high patient attrition. In order to incentivise drug developers, the disease has been designated an orphan disease in the US. Using HFS, Praedicare examined the standard of care in five MAC strains that mimic the heterogenous treatment response encountered in patients. Praedicare then added cutting-edge mathematical modeling systems mapped to individual-level patient response for several diseases, in a Systems of Systems (SoS) approach. The research team accelerated the drug development process by predicting the superiority of a common antibiotic, ceftriaxone, at curing diverse strains of MAC from 10,000 virtual patients at three times shorter time-to-cure than the current three drug combo standard of care. With this virtual clinical trial clarifying the best dosage, rate of bacterial response per day, and shortest treatment duration for different patient profiles, researchers can predict phase III trial outcomes such as clearance of bacteria from sputum (termed sustained sputum culture conversion) even before the first real clinical trial of ceftriaxone for MAC lung disease.

Praedicare’s hollow fiber systems have long been utilized in TB treatment research, but their SoS approach can be generalized to the development of other antimicrobials, and this same approach is being used for liver diseases, cancer, and other non-infectious diseases. The company aims to reduce drug development costs 90%, accelerate the development cycle by 2-3 times, and significantly improve patient safety by supporting many types of biopharmaceutical research efforts in diverse diseases.

“Historically, nine of every ten drug candidates fail clinical trials. But at Praedicare, we’ve crossed into a new era of pharma development. We can now utilize our vast libraries of patient profiles in tandem with wet lab models, mathematical models, and AI to accurately predict whether molecules will achieve disease clearance, making the process of drug development faster and more effective, with less risk to patients,” says Dr. Tawanda Gumbo, CEO of Praedicare and lead researcher for the trial.

“Our intent is to save money, time and lives in the process, and these virtual clinical trial findings could significantly improve the odds of patients surviving MAC,” adds co-author Dr. Gesham Magombedze, who leads the mathematical modeling and AI team at Praedicare and was instrumental in developing the approach.

With the FDA Modernization Act of 2022 allowing animal testing alternatives to reflect scientific advancements in drug development, Praedicare hopes to conduct many more virtual pre-clinical trials.

About Praedicare

Praedicare customizes preclinical models mapped to patients for quantitative prediction of clinical trial outcomes, significantly reducing risk, time and costs of developing safe and effective new drugs.

Praedicare’s revolutionary approach accelerates drug development, reducing clients’ timelines for drug discovery 2-3X, costs of drug development by >90%, and saving patient-volunteer lives by 80%. Based in Dallas, TX, their mission is to use quantitative forecasting, AI, and innovative laboratory tools to heal the world of life-threatening diseases at an affordable price.