Using chronobiology-based second-generation artificial intelligence digital system for overcoming antimicrobial drug resistance in chronic infections

Abstract

Antimicrobial resistance results from the widespread use of antimicrobial agents and is a significant obstacle to the effectiveness of these agents. Numerous methods are used to overcome this problem with moderate success. Besides efforts of antimicrobial stewards, several artificial intelligence (AI)-based technologies are being explored for preventing resistance development. These first-generation systems mainly focus on improving patients’ adherence. Chronobiology is inherent in all biological systems. Host response to infections and pathogens activity are assumed to be affected by the circadian clock. This paper describes the problem of antimicrobial resistance and reviews some of the current AI technologies. We present the establishment of a second-generation AI chronobiology-based approach to help in preventing further resistance and possibly overcome existing resistance. An algorithm-controlled regimen that improves the long-term effectiveness of antimicrobial agents is being developed based on the implementation of variability in dosing and drug administration times. The method provides a means for ensuring a sustainable response and improved outcomes. Ongoing clinical trials determine the effectiveness of this second-generation system in chronic infections. Data from these studies are expected to shed light on a new aspect of resistance mechanisms and suggest methods for overcoming them. IMPORTANCE SECTION The paper presents the establishment of a second-generation AI chronobiology-based approach to help in preventing further resistance and possibly overcome existing resistance. Key messages Antimicrobial resistance results from the widespread use of antimicrobial agents and is a significant obstacle to the effectiveness of these agents. We present the establishment of a second-generation AI chronobiology-based approach to help in preventing further resistance and possibly overcome existing resistance.

Department of Medicine 2040: Implementing a Constrained Disorder Principle-Based Second-Generation Artificial Intelligence System for Improved Patient Outcomes in the Department of Internal Medicine

“… 1 , 2 , 70 , 71 Dosing and medication administration times are altered in these systems to ensure sustainable responses to treatments. 62, 70 - 92 CDP-based systems can also improve digital twin accuracy by accounting for biological noise in a personalized manner. An illustration of how second-generation AI systems can be implemented into therapies is shown in Figure 1D .…”

The Constrained-Disorder Principle Assists in Overcoming Significant Challenges in Digital Health: Moving from “Nice to Have” to Mandatory Systems

“…In addition, there is marked inter and intra-subject variability in response to chronic therapies [ 239 , 240 , 241 , 242 ]. The CDP-based second-generation AI systems provide a platform for overcoming drug resistance and improving adherence by implementing variability-based therapeutic regimens for patients with chronic diseases [ 76 , 108 , 242 , 243 , 244 , 245 , 246 , 247 , 248 , 249 , 250 , 251 , 252 , 253 , 254 , 255 , 256 , 257 , 258 , 259 , 260 ]. The system enables personalized therapies based on individual variability signatures [ 146 , 239 , 257 , 261 ].…”

“…Since patients are the “kings of healthcare,” digital health must transform from a stand-alone product to a service that supports clinical outcomes [ 76 , 108 , 217 , 243 , 244 , 245 , 246 , 247 , 248 , 249 , 250 , 251 , 252 , 253 , 254 , 255 , 256 , 257 , 258 , 259 , 260 , 263 , 264 , 265 , 266 , 267 ]. Unlike first-generation systems, second-generation AI systems are outcome-based, with clinically meaningful endpoints.…”

Using Constrained-Disorder Principle-Based Systems to Improve the Performance of Digital Twins in Biological Systems

“…Second-generation AI systems, which quantify signatures of biological variabilities and implement them into treatment algorithms dynamically, were proposed for overcoming the loss of response to medications [ 16 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 156 , 157 , 158 ]. Second-generation algorithms were found to account for dynamicity in response to therapies that characterized each subject [ 135 ].…”