Order Through Disorder: The Characteristic Variability of Systems

2021

IJERPH

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Background and Aims: Improving global health requires making current and future drugs more effective and affordable. While healthcare systems around the world are faced with increasing costs, branded and generic drug companies are facing the challenge of creating market differentiators. Two of the problems associated with the partial or complete loss of response to chronic medications are a lack of adherence and compensatory responses to chronic drug administration, which leads to tolerance and loss of effectiveness. Approach and Results: First-generation artificial intelligence (AI) systems do not address these needs and suffer from a low adoption rate by patients and clinicians. Second-generation AI systems are focused on a single subject and on improving patients’ clinical outcomes. The digital pill, which combines a personalized second-generation AI system with a branded or generic drug, improves the patient response to drugs by increasing adherence and overcoming the loss of response to chronic medications. By improving the effectiveness of drugs, the digital pill reduces healthcare costs and increases end-user adoption. The digital pill also provides a market differentiator for branded and generic drug companies. Conclusions: Implementing the use of a digital pill is expected to reduce healthcare costs, providing advantages for all the players in the healthcare system including patients, clinicians, healthcare authorities, insurance companies, and drug manufacturers. The described business model for the digital pill is based on distributing the savings across all stakeholders, thereby enabling improved global health.

Section: Second-generation Ai Systems: Focus On Improving Clinicalmentioning

confidence: 99%

Improving Global Healthcare and Reducing Costs Using Second-Generation Artificial Intelligence-Based Digital Pills: A Market Disruptor

2021

IJERPH

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“…Ignoring this problem is associated with lower effectiveness in many drugs used for chronic conditions, Ilan J Transl Med (2021) 19:55 sometimes in up to 40% of patients, such as those with epilepsy or depression, where many patients develop drug resistance [20,21]. Thus, next-generation translation research is required to provide a way to deal with compensatory responses [22][23][24][25][26][27][28][29][30].…”

Section: Next-generation Translational Research: Translating the Innomentioning

confidence: 99%

Why scientists, academic institutions, and investors fail in bringing more products to the bedside: the Active Compass model for overcoming the innovation paradox

2021

J Transl Med

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The vast majority of good science and excellent ideas do not translate into products. Many good products that have the potential to assist in diagnosis and therapy do not mature into everyday care. This often becomes a source of frustration for innovators, academic institutions, companies both small and large, and investors. The “innovation paradox” , wherein excellent ideas and good science fail to reach the bedside, is a major challenge. This study presents the Active Compass model as a way to overcome this obstacle. The model is designed to assist projects at early stages by redirecting and reshaping them in a way that increases their chances of reaching the markets. The model is based on the use of next-generation translational research and on creating differentiators at the early stages of development. The proposed model’s implementation by innovators, scientists, technology transfer offices, academic institutions, analysts, and investors can help move forward high-potential projects to improve the quality of life and alleviate the burdens of disease.

“…A stepwise approach is used for developing a platform for improving the effect of ASD [215][216][217]. Version 1.0 implements a random-number generator for introducing variability in dosing and time drug administration.…”

Section: Variability In Biological Systems and Response To Asdmentioning

confidence: 99%

The role of chronobiology in drug-resistance epilepsy: The potential use of a variability and chronotherapy-based individualized platform for improving the response to anti-seizure drugs

Potruch

Khoury

2020

Seizure

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Despite progress in the development of anti-seizure drugs, drug-resistant epilepsy (DRE) occurs in a third of patients. DRE is associated with poor quality of life and increased risk of sudden, unexplained death. The autonomic nervous system and chronobiology play a role in DRE. In the present paper, we provide a narrative review the mechanisms that underlie DRE and characterize some of the autonomic-and chronotherapy-associated parameters that contribute to the degree of response to therapy. Variability describes the functions of many biological systems, which are dynamic and continuously change over time. These systems are required for responses to continuing internal and external triggers, in order to maintain homeostasis and normal function. Both intra-and inter-subject variability in biological systems have been described. We present a platform, which comprises a personalized-based machine learning closed loop algorithm built on epilepsy-related signatures, autonomic signals, and chronotherapy, as a means for overcoming DRE, improving the response, and reducing the toxicity of current therapies.