CAUSAL ANALYTICS

Redefining analytics into an end-to-end

citizen to patient journey.

Welcome to Accurate Medicine.

CAUSAL ANALYTICS

Redefining analytics into an end-to-end

citizen to patient journey.

Welcome to Accurate Medicine.

ACCURATE MEDICINE

Whereas Precision Medicine focuses on the application of new tech, e.g. genomics, Accurate Medicine emphasizes understanding the complexities of the patient and of the disease, and the real world practice of medicine, recognizing their interrelationships and dependencies in real-world medicine.

INNOVATIVE SOLUTIONS

IPQ Analytics offers a suite of innovative solutions designed and delivered through a service-based approach spanning key inflection points throughout the drug development life cycle designed to optimize target selection, improve clinical trial success and enhance patient outcomes.

Artificial Intelligence (AI)

The State of Precision Medicine AI for Therapeutics: Upstream Applications; Downstream Challenges

Applying evolving technologies to “look for our keys under the lamppost” may make the search go faster, but not necessarily find them more than 10% of the time. - M.Liebman

Published 02/24 - Inside Precision Medicine

AI, Dogma, and Drug Development

Challenge

Biology embraces the existence of a central dogma, i.e., DNA → RNA → protein, and drug development has embraced its own, i.e., disease → target → drug, but both recognize the complexity that governs those transitions. The average cost of developing a drug approved for human use approaches $2 billion and takes 14 years on average, but also realizes a failure rate of approximately 90%. This limited rate of success suggests that significant challenges may exist at the system level, warranting the acquisition, testing, and incorporation of innovative approaches and technologies to address this disparity. Notably, the use of artificial intelligence (AI), machine learning (ML), and big data represent the latest wave of methods to be tested.

Current Approaches

Advances in and access to AI and ML approaches, including deep learning, have significantly impacted many aspects of drug discovery as noted above, enabling the analysis of more complex and larger quantities of data. The results of these analyses are promising as they attempt to address challenges in drug development. However, these methods remain primarily correlative and not causal. AI has been used to both identify a target and design a drug candidate, INSO18_055 (Insilico Medicine), which is now in clinical trials. But to date, there have been no FDA-approved drugs developed solely using AI methods. The goal is to use AI to reduce the failure rate in drug development and produce more effective therapeutics to improve patient care. But instead of asking whether AI will be used to design and develop drugs, it would be better to acknowledge that going forward, all drugs will be developed with the application of AI, whether in discovery or clinical trial development. In the 1970s, the question was “what drugs were developed by molecular modeling and molecular graphics?” The answer was “none, but all drugs are developed using these methods.”

Problem

While the results of these efforts, especially the application of AI and ML, are notable, they were focused on target selection and drug development rather than potentially critical aspects of diseases. The complexities of a disease and its accurate diagnosis are fundamental factors affecting root cause analysis of the current failure rate in drug development.

Solution

A re-focusing: Disease → Target → Drug
Although addressing the disease component of the drug development dogma may seem like an opportunity to reduce the current failure rate, it is not necessarily easy to accomplish by the computational or experimental approaches used for target identification and lead compound screening. Current estimates are that approximately 50–100 million (10%) of all conditions are misdiagnosed every year, resulting in 795,000 deaths and disabilities. This can significantly impact the accurate assignment of targets for drug development and undercut the need to stratify diseases into subtypes, which enables more personalized medicine.

Realized Return/Value

The optimal return on investment for implementing methods like AI/ML will be realized when they are used to address the questions that remain as “unknown unknowns.” Applying evolving technologies to “look for our keys under the lamppost” may make the search go faster, but not necessarily find them more than 10% of the time.

Link: https://www.insideprecisionmedicine.com/news-and-features/ai-dogma-and-drug-development/

ABOUT

IPQ Analytics, LLC (IPQ) is a models as a service (MaaS) company that works globally with industry

(life sciences, pharma, device, diagnostics; across silos), payers, providers, investment banks, associations and government agencies.

IPQ builds computational models of disease, from the clinic back to molecular processes, that reflect the real-world complexities of the patient, the disease and the healthcare environment, including physician and drug developer.

These models are instantiated within a novel knowledge graph that serves as a learning system and continues to evolve through application into new diseases and conditions. IPQ uses this approach to identify critical gaps and conflicts in existing data/knowledge, to treat disease as a process to support the longitudinal stratification of disease, patients and diagnoses, and also deal with the ongoing changes in our understanding that take place over time.

Origin Story

Managing Director and Co-Founder, Michael Liebman, PhD, shares how IPQ Analytics LLC began in 2011

IPQ Analytics boldly redefines the patient journey... through unparalled innovative disease stratification optimizing therapeutic efficacy and outcomes

IPQ Analytics

CAUSAL ANALYTICS