CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence

Abstract

The clinical team attending to a patient upon a diagnosis is faced with two main questions: what treatment, and at what dose? Clinical trials’ results provide the basis for guidance and support for official protocols that clinicians use to base their decisions upon. However, individuals rarely demonstrate the reported response from relevant clinical trials, often the average from a group representing a population or subpopulation. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual’s response to the treatment varies over time with the changes in his or her condition, whether via the indication or physiology. In practice, the drug and the dose selection depend greatly on the medical protocol of the healthcare provider and the medical team’s experience. As such, the results are inherently varied and often suboptimal. Big data approaches have emerged as an excellent decision-making support tool, but their application is limited by multiple challenges, the main one being the availability of sufficiently big datasets with good quality, representative information. An alternative approach—phenotypic personalized medicine (PPM)—finds an appropriate drug combination (quadratic phenotypic optimization platform [QPOP]) and an appropriate dosing strategy over time (CURATE.AI) based on small data collected exclusively from the treated individual. PPM-based approaches have demonstrated superior results over the current standard of care. The side effects are limited while the desired output is maximized, which directly translates into improving the length and quality of individuals’ lives.

Artificial Intelligence in Cancer Research and Precision Medicine

“…109). In addition to monitoring patient responses to therapies, machine learning models such as CURATE.AI now offer additional avenues to adjust drug dosage for single or combination therapies for individual patients in a dynamic manner using patient-specific data points collected over time (110).…”

Automation in pharmaceutical sector by implementation of artificial intelligence platform: a way forward

“…19 Implementation of AI in the development of a pharmaceutical product from the bench to the bedside can be predicted given that it can aid rational drug design, decision making assistance, establish the appropriate treatment for a patient, including personalized medicines, manage the clinical data produced and utilize it for future drug development. 20,21 Hence, AI assistance to pharmaceutical industries helps to improve overall life cycle of product.…”

Artificial Intelligence and Medical Innovation

“…For example, an interdisciplinary team that harnessed the power of CURATE.AI-an AI platform-was able to continuously identify the optimal doses of a drug treatment programme for the successful treatment of cancer and other diseases. 21,22 The second approach is closely related to the enhancement of human attributes such as holistic thinking, emotional intelligence and imaginative problem-solving. When their capabilities in these areas are strengthened, human actors will be better placed to reap greater synergy and positive outcomes from their partnership with AI.…”