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Hurvitz N, Dinur T, Revel-Vilk S, Agus S, Berg M, Zimran A, Ilan Y. A Feasibility Open-Labeled Clinical Trial Using a Second-Generation Artificial-Intelligence-Based Therapeutic Regimen in Patients with Gaucher Disease Treated with Enzyme Replacement Therapy. J Clin Med 2024; 13:3325. [PMID: 38893036 PMCID: PMC11172426 DOI: 10.3390/jcm13113325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2024] [Revised: 05/25/2024] [Accepted: 05/26/2024] [Indexed: 06/21/2024] Open
Abstract
Background/Objectives: Gaucher Disease type 1 (GD1) is a recessively inherited lysosomal storage disorder caused by a deficiency in the enzyme β-glucocerebrosidase. Enzyme replacement therapy (ERT) has become the standard of care for patients with GD. However, over 10% of patients experience an incomplete response or partial loss of response to ERT, necessitating the exploration of alternative approaches to enhance treatment outcomes. The present feasibility study aimed to determine the feasibility of using a second-generation artificial intelligence (AI) system that introduces variability into dosing regimens for ERT to improve the response to treatment and potentially overcome the partial loss of response to the enzyme. Methods: This was an open-label, prospective, single-center proof-of-concept study. Five patients with GD1 who received ERT were enrolled. The study used the Altus Care™ cellular-phone-based application, which incorporated an algorithm-based approach to offer random dosing regimens within a pre-defined range set by the physician. The app enabled personalized therapeutic regimens with variations in dosages and administration times. Results: The second-generation AI-based personalized regimen was associated with stable responses to ERT in patients with GD1. The SF-36 quality of life scores improved in one patient, and the sense of change in health improved in two; platelet levels increased in two patients, and hemoglobin remained stable. The system demonstrated a high engagement rate among patients and caregivers, showing compliance with the treatment regimen. Conclusions: This feasibility study highlights the potential of using variability-based regimens to enhance ERT effectiveness in GD and calls for further and longer trials to validate these findings.
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Affiliation(s)
- Noa Hurvitz
- Departments of Medicine and Neurology, Hadassah Medical Center, Jerusalem 9112001, Israel;
| | - Tama Dinur
- Gaucher Unit, The Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel; (T.D.); (S.R.-V.); (A.Z.)
| | - Shoshana Revel-Vilk
- Gaucher Unit, The Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel; (T.D.); (S.R.-V.); (A.Z.)
- Faculty of Medicine, Hebrew University, Jerusalem 9112001, Israel
| | - Samuel Agus
- Oberon Sciences and Area 9 Innovation, Chestnut Hill, MA 02467, USA; (S.A.); (M.B.)
| | - Marc Berg
- Oberon Sciences and Area 9 Innovation, Chestnut Hill, MA 02467, USA; (S.A.); (M.B.)
- Stanford University, Palo Alto, CA 94305, USA
| | - Ari Zimran
- Gaucher Unit, The Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel; (T.D.); (S.R.-V.); (A.Z.)
- Faculty of Medicine, Hebrew University, Jerusalem 9112001, Israel
| | - Yaron Ilan
- Departments of Medicine and Neurology, Hadassah Medical Center, Jerusalem 9112001, Israel;
- Faculty of Medicine, Hebrew University, Jerusalem 9112001, Israel
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Ilan Y. Next-Generation Personalized Medicine: Implementation of Variability Patterns for Overcoming Drug Resistance in Chronic Diseases. J Pers Med 2022; 12:jpm12081303. [PMID: 36013252 PMCID: PMC9410281 DOI: 10.3390/jpm12081303] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/05/2022] [Accepted: 08/08/2022] [Indexed: 12/14/2022] Open
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.
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Affiliation(s)
- Yaron Ilan
- Department of Medicine, Hadassah Medical Center, Faculty of Medicine, Hebrew University, Jerusalem POB12000, Israel
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Kenig A, Kolben Y, Asleh R, Amir O, Ilan Y. Improving Diuretic Response in Heart Failure by Implementing a Patient-Tailored Variability and Chronotherapy-Guided Algorithm. Front Cardiovasc Med 2021; 8:695547. [PMID: 34458334 PMCID: PMC8385752 DOI: 10.3389/fcvm.2021.695547] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 07/21/2021] [Indexed: 01/12/2023] Open
Abstract
Heart failure is a major public health problem, which is associated with significant mortality, morbidity, and healthcare expenditures. A substantial amount of the morbidity is attributed to volume overload, for which loop diuretics are a mandatory treatment. However, the variability in response to diuretics and development of diuretic resistance adversely affect the clinical outcomes. Morevoer, there exists a marked intra- and inter-patient variability in response to diuretics that affects the clinical course and related adverse outcomes. In the present article, we review the mechanisms underlying the development of diuretic resistance. The role of the autonomic nervous system and chronobiology in the pathogenesis of congestive heart failure and response to therapy are also discussed. Establishing a novel model for overcoming diuretic resistance is presented based on a patient-tailored variability and chronotherapy-guided machine learning algorithm that comprises clinical, laboratory, and sensor-derived inputs, including inputs from pulmonary artery measurements. Inter- and intra-patient signatures of variabilities, alterations of biological clock, and autonomic nervous system responses are embedded into the algorithm; thus, it may enable a tailored dose regimen in a continuous manner that accommodates the highly dynamic complex system.
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Affiliation(s)
- Ariel Kenig
- Department of Medicine, Hebrew University-Hadassah Medical Center, Jerusalem, Israel
| | - Yotam Kolben
- Department of Medicine, Hebrew University-Hadassah Medical Center, Jerusalem, Israel
| | - Rabea Asleh
- Department of Cardiology, Hebrew University-Hadassah Medical Center, Jerusalem, Israel
| | - Offer Amir
- Department of Cardiology, Hebrew University-Hadassah Medical Center, Jerusalem, Israel
- The Azrieli Faculty of Medicine in the Galilee, Bar-Ilan University, Safed, Israel
| | - Yaron Ilan
- Department of Medicine, Hebrew University-Hadassah Medical Center, Jerusalem, Israel
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Avolio A. The Hidden Diagnostic Information of the Heart Rhythm Revealed by Entropy Proportions. Pulse (Basel) 2021; 8:75-77. [PMID: 34307202 PMCID: PMC8280413 DOI: 10.1159/000516822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 04/21/2021] [Indexed: 11/19/2022] Open
Affiliation(s)
- Alberto Avolio
- Department of Biomedical Sciences, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, New South Wales, Australia
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