Next-Generation Personalized Medicine: Implementation of Variability Patterns for Overcoming Drug Resistance in Chronic Diseases

Abstract

Chronic diseases are a significant healthcare problem. Partial or complete non-responsiveness to chronic therapies is a significant obstacle to maintaining the long-term effect of drugs in these patients. A high degree of intra- and inter-patient variability defines pharmacodynamics, drug metabolism, and medication response. This variability is associated with partial or complete loss of drug effectiveness. Regular drug dosing schedules do not comply with physiological variability and contribute to resistance to chronic therapies. In this review, we describe a three-phase platform for overcoming drug resistance: introducing irregularity for improving drug response; establishing a deep learning, closed-loop algorithm for generating a personalized pattern of irregularity for overcoming drug resistance; and upscaling the algorithm by implementing quantified personal variability patterns along with other individualized genetic and proteomic-based ways. The closed-loop, dynamic, subject-tailored variability-based machinery can improve the efficacy of existing therapies in patients with chronic diseases.

Improving the effectiveness of anti-aging modalities by using the constrained disorder principle-based management algorithms

“…They use but are independent of big data sets, which are the basis for most first-generation AI systems that lack accuracy and relevance to a single subject. Most big data sets prevent an ability of dynamicity, which is behind every biological function ( Ilan, 2020c ; Ilan, 2020d ; Ilan, 2021b ; Ilan, 2022 ).…”

“…It enables tailoring the variability regiments to the patient’s response in a dynamic way that keeps changing. In the third level, the system quantifies the variability measures and implements them into the algorithm for generating therapeutic regimens to improve the response rate to chronic anti-aging therapies ( Ilan, 2020c ; Ilan, 2020d ; Ilan, 2021b ; Ilan, 2022 ). Quantifying measures of variability and implementing them into the algorithm in a patient-tailored dynamic way provide a method to incorporate personalized variabilities into treatment algorithms aiming at overcoming the loss of response ( Ilan, 2021b ).…”

The Constrained Disorder Principle May Account for Consciousness

“…A CDP implies that randomness and variability are essential for improved function [ 8 ]. According to the CDP, variability can overcome adaptation to chronic signals [ 274 , 275 , 276 , 277 ]. During each disturbance, the organism gradually learns how to deal with recurrent changes in its internal environment to keep functioning optimally through adaptation [ 278 ].…”

The constrained disorder principle defines living organisms and provides a method for correcting disturbed biological systems

“…They implement variability within pre-defined personalized boundaries into therapeutic regimens designed to improve clinical outcomes using personalized signatures of variability intrinsic to health and disease states [164] , [165] , [166] . The algorithms quantify variability signatures relevant to the disease, such as HRV in patients with cardiac diseases, variability in cytokines secretion in patients with inflammatory disorders, or variability in electro-encephalographic tests in patients with epilepsy, and implement them into the treatment regimens [10] , [143] , [167] , [168] , [169] , [170] , [171] , [172] , [173] , [174] , [175] , [176] , [177] , [178] , [179] , [180] , [181] , [182] , [183] , [184] .…”