Introduction
Dimethyl fumarate (DMF) is an oral disease-modifying therapy (DMT) recommended as first-line therapy for relapsing-remitting (RR) multiple sclerosis (MS) patients ( Montes Diaz et al., 2018 ). DMF exhibits neuroprotective and immunomodulatory effects, along with a favorable benefit-to-risk profile. It remains an effective treatment for MS, with a favorable safety profile demonstrated over ten years of clinical use ( Gold et al., 2020 , Chinea et al., 2020 ). DMF is effective and safe for treating MS in real-world audits. Treatment-naive patients see significant benefits from DMF, and it also reduces the annualized relapse rate (ARR) in patients who switch from injectable therapies due to issues with tolerability and efficacy ( Barros et al., 2020 ).
Lack of efficacy (50%), safety concerns (30%), tolerability issues (7%), and poor compliance (7%) are the reasons for DMF discontinuation over time. Higher pre-treatment EDSS is associated with DMF discontinuation ( Lanzillo et al., 2020 ). Patients discontinued DMF due to side effects in 22% of cases, with gastrointestinal discomfort (12.7%) and lymphopenia (5.3%) being the most frequently reported ( Miclea et al., 2016 ). In most cases, loss of response to DMF leads to replacing the treatment with other immunomodulatory medications that are not necessarily better or safer, some of which carry high side effects.
Second-generation artificial intelligence (AI) systems use variability-based algorithms to create personalized treatment plans by optimizing dosages and administration times. These systems prioritize clinical outcomes as endpoints for their algorithms. They dynamically adjust their outputs based on ongoing assessments of patient responses and adhere to the n = 1 concept, which focuses on individual subjects ( Bayatra et al., 2024 , Ilan, 2020a , Ilan, 2021a , Ilan, 2024 ). The platform addresses the compensatory mechanisms underlying the decreased effectiveness of chronic medications ( Ilan, 2020a , Ilan, 2021b , Ilan, 2022a ). The platform is founded on the Constrained Disorder Principle (CDP), which characterizes biological systems by their variability within dynamic boundaries. According to the CDP, malfunctions in biological systems occur either when inherent variability is lost or when variability exceeds established biological limits. Additionally, a second-generation AI system aids patients through an app that manages their medication schedules and dosages ( Ilan, 2021b , Ilan, 2022b ).
In the context of DMF, its primary mechanisms—Nrf2 pathway activation and modulation of immune cell trafficking via lymphocyte subtype shifts—may be subject to regulatory adaptation with fixed chronic dosing, providing a mechanistic rationale for introducing dosing variability ( Hammer et al., 2018 ). However, direct MS-specific evidence for this approach remains to be established.
This open-label feasibility clinical trial aimed to assess the feasibility, safety, and tolerability of second-generation AI-regulated treatment regimens in potentially enhancing the response to DMF, and to generate preliminary exploratory data on clinical and biological signals to inform future controlled trials.
Methods
Study design
The clinical trial was a 12-week, open-label, uncontrolled, prospective feasibility study conducted at a single center. The primary purpose was to assess the feasibility, safety, and tolerability of the intervention. All clinical outcome measures (EDSS, NfL, MRI) were prespecified as exploratory signals only and were not powered to infer efficacy. Participants were recruited for the trial at Hadassah-Hebrew University Medical Center in Jerusalem, Israel. The Hadassah Medical Center IRB and the Israeli Ministry of Health (MOH_2022–07–07_011892) approved the trial. The trial was registered at ClinicalTrials.gov: NCT06385197 .
Participant flow
Of 128 screened patients, 121 were excluded for the following reasons: 68 did not meet the inclusion criteria (including the smartphone requirement and continuous DMF use ≥6 months without interruption); 34 declined to participate; and 19 were excluded for other reasons (comorbidities, inability to attend visits). Seven patients were enrolled and analyzed.
Study population
Eligible subjects included those aged 18–75 years, male and female, diagnosed with MS.
Inclusion and exclusion criteria
The study included adult non-pregnant patients diagnosed with multiple sclerosis (MS) and treated with dimethyl fumarate (DMF) without any other immunosuppressive treatment or steroid use for at least six months. Patients with severe infectious, malignant, autoimmune, or other systemic diseases were excluded from the study. Additionally, patients who could not provide written informed consent, lacked a smartphone, or failed to adhere to the visit schedule and protocol were excluded.
Second-generation AI system
Altus Care™ is a mobile application developed by Area9 Innovation Apps (version 2.1). The system used in this study operated in Level 1 (open-loop) mode, in which the algorithm applies fully randomized dosing and timing within physician-defined ranges, without adaptive feedback from clinical outcomes during the trial. The permitted DMF dose range was the approved therapeutic range (120–480 mg/day) as specified by the treating neurologist for each patient. Administration times were randomized within a clinician-specified morning and/or evening window. In the event of a missed dose, the app sent a re-notification within 30 min; if unacknowledged, the dose was skipped per protocol. Clinicians retained full override authority and could modify dose ranges at any study visit. The system inputs were the physician-defined ranges; the outputs were daily push notifications to the patient specifying dose and timing. No dose exceeded what the patient was already receiving before enrollment, and no administration was more frequent than the pre-enrollment schedule.
Feasibility endpoints (Prespecified)
The following feasibility endpoints were prespecified: Successful installation and use of the Altus Care™ app Patient adherence to the algorithmic dosing schedule (target: ≥80% of doses taken as notified) Completion of the 12-week follow-up period Absence of serious adverse events attributable to the variable dosing regimen
Follow-up parameters
Throughout the follow-up period, the research coordinator made weekly phone calls to check clinical well-being and adherence. Physical examination and EDSS were assessed at weeks 6 and 12. NfL levels were measured before and after the intervention (Simoa pTau-181 Advantage v2.1 HD-1/JD-X kit, Quanterix). MRI was performed on a 3 T scanner (Philips Ingenia) at Hadassah Medical Center using a standardized protocol including T1, T2, FLAIR, and post-gadolinium T1 sequences. The baseline MRI was the most recent scan available within 6 months before enrollment. A follow-up MRI was performed within 12 months of study completion. Lesion assessment was performed by a board-certified neuroradiologist blinded to clinical data. Volumetric analysis was performed using Slicer 3D software. Safety monitoring included a complete blood count and hepatic function tests at baseline and at study end.
Note: All clinical outcome measures were exploratory. The study was not powered to detect differences in efficacy. The contributions of variable dosing and adherence support cannot be disentangled in this study design, as all patients received both the algorithmic variability intervention and app reminders, as well as weekly contact with a coordinator.
Statistics
The Wilcoxon signed-rank test was used to analyze paired, continuous, non-parametric data.
Results
Subjects' demographics and safety measures
Table 1 presents the demographics of the patients who participated in the study. Seven patients were enrolled, two males and five females, with an average age of 37. All patients completed the 12-week follow-up period without experiencing any significant adverse events. Table 1 Baseline characteristics. Age, median (range) 37 (28−54) Male, number (%) 2 (29) Time since diagnosis of MS, median (range), Y 5 (3−21) Time since DMF treatment, median (range) M 51.5 (24–87.2) Concomitant diseases Radiculopathy n (%) 1 (7) Migraine (%) 1 (7)
Table 1
Baseline characteristics.
| Age, median (range) | 37 (28−54) | |
| Male, number (%) | 2 (29) | |
| Time since diagnosis of MS, median (range), Y | 5 (3−21) | |
| Time since DMF treatment, median (range) M | 51.5 (24–87.2) | |
| Concomitant diseases | Radiculopathy n (%) | 1 (7) |
| Migraine (%) | 1 (7) |
Safety data
Lymphocyte counts were measured at baseline and study end. All patients maintained lymphocyte counts above 0.5 × 10⁹/L throughout the study. No serious infections were recorded. Gastrointestinal adverse effects (nausea, diarrhea, abdominal pain) were reported in 2/7 patients (29%) and were mild and self-limiting. Flushing was reported in 14% of patients (1/7). No treatment interruptions or discontinuations occurred. CBC and hepatic function tests were within normal limits at all timepoints. Safety data are presented in Supplementary Table S1.
Adding variability in administration times may improve the clinical outcome of MS patients
Exploratory clinical outcomes
The second-generation, AI-based, personalized regimen was feasible and well-tolerated. The following data represent exploratory signals and should not be interpreted as evidence of efficacy. The EDSS score improved in 2 patients (29%, p = 0.371) by a mean of 0.5 points, and did not change in 5 patients (71%). No patients had a worsening of their EDSS score ( Fig. 1 A). These results were not statistically significant and are reported only as directional signals. Fig. 1 A: The effect of the intervention on the EDSS score was measured in all patients before and after the intervention. Each bar represents an individual patient before and after the intervention. 1B: Effect of intervention on disease progression. EDSS during the intervention was compared with its pre-intervention progression. Each line represents an individual patient. 1 C: The effect of the intervention on neurofilament light chains was measured in all patients before and after the intervention. Each bar represents an individual patient before and after the intervention. 1D: Effect of MRI intervention. Following the intervention, MRI was available in 6 patients. No change was noted in 6 patients (100%) compared with pre-intervention MRI. Volumetric analysis also demonstrated stability, with an average delta of 2.06 cm 3 . Each line represents an individual patient. 1E: Percentage of patients who adhere to treatment regimens and use the app daily.
NfL levels decreased in 5/7 patients (71%, p = 0.375), with an average reduction of 1.5 pg/mL ( Fig. 1 C). This did not reach statistical significance. Six patients had MRIs available following the intervention. Compared with the pre-intervention MRI, no new T2- or gadolinium-enhancing lesions were noted in any of the six patients, and volumetric analysis showed stability, with an average delta of 2.06 cm³ ( Fig. 1 D).
Six patients used the app daily, as indicated by phone questionnaires. The seventh patient did not use the app daily but still adhered to the prescribed regimen. All patients adhered to the treatment regimens as indicated by the push notifications ( Fig. 1 E). These results indicate that the prespecified feasibility endpoints were met.
Discussion
This open-label, exploratory, proof-of-concept feasibility clinical trial involving a small number of patients suggests that randomizing DMF regimens based on a second-generation personalized AI algorithm is feasible, safe, and may be associated with a preliminary signal of potential benefit. The data indicated that 29% of participants showed improvement in EDSS scores within 12 weeks, and all available MRIs demonstrated stable findings. These results are exploratory and insufficient to support conclusions about efficacy.
The constrained disorder principle (CDP) asserts that biological systems exhibit a degree of disorder within defined limits, and that variability is a hallmark of normal biological function ( Kenig et al., 2021a , Azmanov et al., 2021 , Potruch et al., 2020 , Isahy and Ilan, 2021 , Khoury and Ilan, 2019a , Khoury and Ilan, 2021 , Kenig and Ilan, 2019 , Ilan, 2019a , Ilan, 2019a , Gelman et al., 2020 , Ishay et al., 2021b ). In the context of DMF pharmacology, chronic fixed-dose administration may lead to regulatory adaptation through mechanisms such as normalization of lymphocyte subsets and desensitization of receptor pathways. The introduction of variability in dosing and timing is hypothesized to counteract these compensatory mechanisms. However, we emphasize that this rationale remains speculative in the context of MS, and direct mechanistic evidence specific to DMF and MS biology has not yet been established. The present study was not designed to test this mechanistic hypothesis.
Many patients with chronic diseases experience a loss of effectiveness from their treatments over time ( Ilan, 2021a , Gelman et al., 2020 , Ilan, 2019b , Ilan, 2019c , Ilan, 2020a , Ilan, 2020b , El-Haj et al., 2019 , Ilan, 2019a , Ilan, 2019b , Ilan-Ber and Ilan, 2019 , Forkosh et al., 2020 , Shabat et al., 2021 , Mietus et al., 2002 , Navaneethakrishna and Manuskandan, 2021 , Costa and Goldberger, 2019 , Hammerle et al., 2020 , Sen and McGill, 2018 , Schutte et al., 2022 , van den Bosch et al., 2021 , Boripuntakul et al., 2022 , Genon et al., 2022 ). Regular dosing can lead to decreased response and the development of drug tolerance. Utilizing algorithms based on variability may help address the body's compensatory responses to chronic medications ( Ilan, 2021a , Kessler et al., 2020 , Ishay et al., 2021a , Kolben et al., 2021 , Kenig et al., 2021a , Azmanov et al., 2021 , Potruch et al., 2020 , Isahy and Ilan, 2021 , Khoury and Ilan, 2019a , Khoury and Ilan, 2021 , Kenig and Ilan, 2019 , Ilan, 2019a , Ilan, 2019a , Gelman et al., 2020 , Ishay et al., 2021b , Ilan and Spigelman, 2020 , Hurvitz et al., 2021 , Gelman et al., 2022 , Azmanov et al., 2022 , Hurvitz et al., 2022 , Ilan, 2020b ). CDP-based second-generation AI algorithms implement variability signatures in a personalized manner. These systems utilize a dynamic algorithm that continuously adjusts treatment plans based on important clinical outcomes. The algorithm considers various input factors, including the host's characteristics, the disease, and environmental conditions. It is designed to introduce variations in dosages and administration times of chronic medications to counteract the potential loss of drug effectiveness, ensuring a sustained therapeutic impact ( Kessler et al., 2020 , Ishay et al., 2021a , Kolben et al., 2021 , Kenig et al., 2021a , Azmanov et al., 2021 , Potruch et al., 2020 , Isahy and Ilan, 2021 , Khoury and Ilan, 2019a , Khoury and Ilan, 2021 , Kenig and Ilan, 2019 , Ilan, 2019a , Ilan, 2019a , Gelman et al., 2020 , Ishay et al., 2021b , Ilan and Spigelman, 2020 , Ilan, 2021b , Ilan, 2020b , Sigawi et al., 2023 , Gelman et al., 2023 , Hurvitz et al., 2024 , Sigawi et al., 2024 , Hurvitz et al., 2025 ). CDP-based second-generation AI algorithms implement variability signatures in a personalized manner. The algorithm considers various input factors, including the patient's characteristics, the disease, and the treating physician's defined ranges, and introduces variations in dosages and administration times within clinically approved limits.
The constrained disorder principle (CDP) asserts that biological systems display a certain degree of disorder within defined limits. Improper regulation or malfunction of these boundaries can result in biological dysfunction and disease. Variability is a hallmark of biological systems, indicating the proper function of the genome, organelles, and entire organs ( Ilan, 2019a , Ilan, 2019b , Ilan, 2019c , Ilan, 2020a , Ilan, 2020b , El-Haj et al., 2019 , Ilan, 2019a , Ilan, 2019b , Ilan-Ber and Ilan, 2019 , Forkosh et al., 2020 , Shabat et al., 2021 ). Heart rate variability and fragmentation are intrinsic characteristics of the heart, with reduced variability associated with higher cardiac mortality. Variability is also characteristic of normal breathing, blood pressure, brain function, and gait ( Mietus et al., 2002 , Navaneethakrishna and Manuskandan, 2021 , Costa and Goldberger, 2019 , Hammerle et al., 2020 , Sen and McGill, 2018 , Hurvitz et al., 2021 , Schutte et al., 2022 , van den Bosch et al., 2021 , Boripuntakul et al., 2022 , Genon et al., 2022 , Saha and Baumert, 2019 , Crawford et al., 2022 ).
A second-generation AI system manages the administration of the digital pill regimen, which consists of three levels. At the first level, a fully randomized approach is applied within predefined ranges established in the current trial. Physicians determine these ranges for DMF (dimethyl fumarate) dosages and administration times. Patients receive randomized reminders on their app regarding DMF dosages and administration times, all within clinically proven and regulatory-approved limits. This system operates in an open-loop mode, meaning it functions independently of specific inputs and outputs ( Ilan, 2020a , Ilan, 2021b , Ilan, 2022a , Sigawi et al., 2023 , Ilan, 2022c , Hurvitz and Ilan, 2023 , Sigawi and Ilan, 2023 ).
The second level of the digital pill features a closed-loop system that dynamically adjusts variability ranges based on specific outcome measures. This algorithm takes into account the individual user's responses to customize the variability accordingly. At the third level, the algorithm integrates chronotherapy and considers factors such as heart rate variability, EEG variability, and variability in cytokine secretion ( Bayatra et al., 2024 , Ilan, 2021b ).
Multiple mechanisms contribute to the reduced effectiveness of immunomodulatory agents, posing a challenge for overcoming and improving the response ( Neuhaus et al., 2003 , Khoury and Ilan, 2019b , Kalden and Schulze-Koops, 2017 , Souto et al., 2016 , Fine et al., 2019 , Oleen-Burkey et al., 2012 ). These include genetic diseases, and environmental factors. Many MS patients who receive immunomodulatory agents discontinue them over several years. It is often due to insufficient clinical response or adverse effects ( Yang et al., 2022 ).
The algorithm outlined in this study offers a way to enhance the clinical response to immunomodulatory drugs, regardless of the underlying mechanisms involved ( Ilan, 2020a , Ilan, 2021b , Ilan, 2022a , Sigawi et al., 2023 , Ilan, 2022c , Hurvitz and Ilan, 2023 , Sigawi and Ilan, 2023 ). When administered early in the disease process, this approach could also help prevent resistance in patients with multiple sclerosis (MS) and other immune-mediated disorders. Its simplicity enables implementation for patients with chronic conditions who have either experienced reduced effectiveness from their current medications or have not fully responded to their treatment regimens ( Ilan, 2021a , Ilan, 2022a , Kessler et al., 2020 , Ishay et al., 2021a , Kolben et al., 2021 , Kenig et al., 2021a , Azmanov et al., 2021 , Potruch et al., 2020 , Isahy and Ilan, 2021 , Khoury and Ilan, 2019a , Khoury and Ilan, 2021 , Kenig and Ilan, 2019 , Ilan, 2019a , Ilan, 2019a , Gelman et al., 2020 , Ishay et al., 2021b , Ilan and Spigelman, 2020 , Hurvitz et al., 2021 , Gelman et al., 2022 , Azmanov et al., 2022 , Kenig et al., 2021b ).
Limitations
The current study has several important limitations. First, the sample size of seven patients from a single center severely limits generalizability. Second, the open-label, non-randomized design with no concurrent control group precludes any inference regarding efficacy or noninferiority. Third, the 12-week follow-up period is insufficient to assess long-term outcomes relevant to MS, including relapse rate, disability progression, or MRI lesion accumulation. Fourth, and critically, the study design cannot disentangle the effect of algorithmic dosing variability from the effect of enhanced adherence support, as all patients received both app reminders and weekly coordinator contact, either of which may independently improve adherence and outcomes. Future controlled trials should include an active comparator arm in which participants receive a fixed-dose reminder app with no dosing variability and equivalent coordinator contact to isolate the specific contribution of the variability algorithm. Fifth, the outcome measures used (EDSS, NfL, MRI without gadolinium-enhancing lesion protocol emphasis) are not the most sensitive or standard endpoints for an MS intervention study; future studies should incorporate annualized relapse rate, steroid-treated relapses, new or enlarging T2 lesions, gadolinium-enhancing lesions, and validated cognitive and functional measures.
Summary
The data from this exploratory feasibility study support the safety and feasibility of using a personalized AI algorithm to guide the randomization of DMF regimens and provide hypothesis-generating signal data that may inform future controlled trials. These findings are not sufficient to establish efficacy. Extensive prospective, randomized, controlled studies with appropriate comparator arms and robust MS-specific outcome measures are needed to confirm or refute these preliminary observations.
Authors' contributions
HL, YH, and NH conducted the trial and analyzed the data; SA MB supported the app; AD and YI conceptualized it; and all authors reviewed the final version.
CRediT authorship contribution statement
Adi Vaknin-Dembinsky: Writing – review & editing, Writing – original draft, Formal analysis, Conceptualization. Noa Hurvitz: Writing – review & editing, Writing – original draft, Formal analysis, Data curation. Marc Berg: Writing – review & editing, Writing – original draft, Conceptualization. Yoav Hershkowitz: Writing – review & editing, Writing – original draft, Investigation, Formal analysis, Data curation. Hillel Lehmann: Writing – review & editing, Writing – original draft, Investigation, Formal analysis, Data curation. Samuel Agus: Writing – review & editing, Writing – original draft. Yaron Ilan: Writing – review & editing, Writing – original draft.
Disclosure
YI is the founder of Oberon Sciences. SA is a consultant for Oberon Sciences. MB is a consultant of Area9.
Funding
NA.
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