Treatment response and disease progression

Prediction of Disease Progression and Clinical Response in Systemic Sclerosis: Experience From a Proof-of-Concept Trial

OBJECTIVE

Using the modified Rodnan skin score (mRSS) as a surrogate for disease activity, a phase 2a study in patients with systemic sclerosis (SSc) measured efficacy of the autotaxin inhibitor ziritaxestat. Mathematical modeling of mRSS was used to predict disease progression, examine candidate trial designs, and predict the probability of successfully discriminating treatment effect.

METHODS

Patients with SSc receiving 600 mg of ziritaxestat or placebo for 24 weeks were included, in addition to data up to week 52 of the open-label extension (OLE). Longitudinal mRSS data were described using a disease progression model; drug effect was a binary variable. Parameters used to predict the OLE mRSS outcome were estimated using data from the 24-week double-blind phase and validated with observed data. Three trial designs were simulated to identify which had the highest probability of detecting a treatment effect. Power to detect a treatment effect was quantified using the simulations.

RESULTS

Maximum decreases from baseline in mRSS were 50.4% (ziritaxestat) and 34.7% (placebo). Study designs based on 300 patients randomized 2:1 or 1:1 to 600 mg of ziritaxestat or placebo had similar probabilities of detecting a significant treatment effect. Power to detect a treatment effect was >80% for all simulations.

CONCLUSION

Disease progression and drug effect could be predicted beyond the range of observed data. This modeling and simulation approach may inform future trial design, including study duration, and predict the probability of success.

Pharmacometric modeling of drug adverse effects: an application of mixture models in schizophrenia spectrum disorder patients treated with clozapine

Clozapine has superior efficacy to other antipsychotics yet is underutilized due to its adverse effects, such as neutropenia, weight gain, and tachycardia. The current investigation aimed to introduce a pharmacometric approach to simultaneously model drug adverse effects, with examples from schizophrenia spectrum patients receiving clozapine. The adverse drug effects were represented as a function of time by incorporating a mixture model to describe individual susceptibility to the adverse effects. Applications of the proposed method were presented by analyzing retrospective data from patients' medical records in Psychiatric Clinic, Penang General Hospital. Tachycardia, weight gain, and absolute neutrophils count (ANC) decrease were best described by an offset, a piecewise linear, and a transient surge function, respectively. 42.9% of the patients had all the adverse effects, including weight gain (0.01 kg/m² increase every week over a baseline of 24.7 kg/m² until stabilizing at 279 weeks), ANC decrease (20% decrease from 4540 cells/µL week 12-20.8), and tachycardia (14% constant increase over a baseline of 87.9 bpm for a clozapine maintenance dose of 450 mg daily). 32.5% of the patients had only tachycardia, while the remaining 24.6% had none of the adverse effects. A new pharmacometric approach was proposed to describe adverse drug effects with examples of clozapine-induced weight gain, ANC drop, and tachycardia. The current approach described the longitudinal time changes of continuous data while assessing patient susceptibility. Furthermore, the model revealed the possible co-existence of ANC drop and weight gain; thus, neutrophil monitoring might predict future changes in body weight.

Disease severity and status in Stevens–Johnson syndrome and toxic epidermal necrolysis: Key knowledge gaps and research needs

Stevens–Johnson syndrome and toxic epidermal necrolysis (SJS/TEN) are on a spectrum of cutaneous drug reactions characterized by pan-epidermal necrosis with SJS affecting < 10% of body surface area (BSA), TEN > 30%, and SJS/TEN overlap between 10 and 30%. Severity-of-illness score for toxic epidermal necrolysis (SCORTEN) is a validated tool to predict mortality rates based on age, heart rate, BSA, malignancy and serum urea, bicarbonate, and glucose. Despite improved understanding, SJS/TEN mortality remains constant and therapeutic interventions are not universally accepted for a number of reasons, including rarity of SJS/TEN; inconsistent definition of cases, disease severity, and endpoints in studies; low efficacy of interventions; and variations in treatment protocols. Apart from mortality, none of the other endpoints used to evaluate interventions, including duration of hospitalization, is sufficiently standardized to be reproducible across cases and treatment centers. Some of the gaps in SJS/TEN research can be narrowed through international collaboration to harmonize research endpoints. A case is made for an urgent international collaborative effort to develop consensus on definitions of endpoints such as disease status, progression, cessation, and complete re-epithelialization in interventional studies. The deficiencies of using BSA as the sole determinant of SJS/TEN severity, excluding internal organ involvement and extension of skin necrosis beyond the epidermis, are discussed and the role these factors play on time to healing and mortality beyond the acute stage is highlighted. The potential role of artificial intelligence, biomarkers, and PET/CT scan with radiolabeled glucose as markers of disease status, activity, and therapeutic response is also discussed.

Clinical Variation in the Treatment Practices for Medication Nonadherence, Drug-Drug Interactions, and Recognition of Disease Progression in Patients with Chronic Cardiometabolic Diseases: A Cross-Sectional Patient Simulation Study among Primary Care Physicians

BACKGROUND

Medication nonadherence in patients with chronic diseases is common, costly, and often underdiagnosed. In the United States, approximately 40-50% of patients with cardiometabolic conditions are not adherent to long-term medications. Drug-drug interactions (DDI) are also underrecognized and may lead to medication nonadherence in this patient population. Treatment complexity associated with cardiometabolic conditions contributes to increased risk for adverse drug events and DDIs.

METHODS

We recruited a nationally representative sample of 246 board-certified family and internal medicine physicians to evaluate how they assessed, identified, and treated medication nonadherence, DDIs, and worsening disease. Participating physicians were asked to care for three online simulated patients, each with at least one chronic cardiometabolic disease, including atrial fibrillation, heart failure, diabetes mellitus, or hypertension, and who were taking prescription medications for their disease. Physicians' scores were based on evidence-based care recommendation criteria, including overall care quality and treatment for medication nonadherence and DDIs.

RESULTS

Overall, quality-of-care scores across all cases ranged from 13% to 87% with an average of 50.8% ± 12.1%. The average overall diagnostic plus treatment score was 21.9% ± 13.6%. Participants identified nonadherence in just 3.6% of cases, DDIs in 8.9% of cases, and disease progression in 30.3% of cases.

CONCLUSIONS

Based on these study results, primary care physicians were unable to adequately diagnose and treat patients with chronic cardiometabolic diseases who either suffered from medication nonadherence, DDIs, or progression of the disease. Improved standardization and technique in identifying these diagnoses is needed in primary care. Trial Registration. This trial is registered with clinicaltrials.gov, NCT05192590.

Machine learning-guided, big data-enabled, biomarker-based systems pharmacology: modeling the stochasticity of natural history and disease progression

The incidence of systemic and metabolic co-morbidities increases with aging. The purpose was to investigate a novel paradigm for modeling the orchestrated changes in many disease-related biomarkers that occur during aging. A hybrid strategy that integrates machine learning and stochastic modeling was evaluated for modeling the long-term dynamics of biomarker systems. Bayesian networks (BN) were used to identify quantitative systems pharmacology (QSP)-like models for the inter-dependencies for three disease-related datasets of metabolic (MB), metabolic with leptin (MB-L), and cardiovascular (CVB) biomarkers from the NHANES database. Biomarker dynamics were modeled using discrete stochastic vector autoregression (VAR) equations. BN were used to derive the topological order and connectivity of a data driven QSP model structure for inter-dependence of biomarkers across the lifespan. The strength and directionality of the connections in the QSP models were evaluated using bootstrapping. VAR models based on QSP model structures from BN were assessed for modeling biomarker system dynamics. BN-restricted VAR models of order 1 were identified as parsimonious and effective for characterizing biomarker system dynamics in the MB, MB-L and CVB datasets. Simulation of annual and triennial data for each biomarker provided good fits and predictions of the training and test datasets, respectively. The novel strategy harnesses machine learning to construct QSP model structures for inter-dependence of biomarkers. Stochastic modeling with the QSP models was effective for predicting the age-varying dynamics of disease-relevant biomarkers over the lifespan.