Disease progression and neuroscience

Challenges in Alzheimer's Disease Drug Discovery and Development: The Role of Modeling, Simulation, and Open Data

Alzheimer's disease (AD) is the leading cause of dementia worldwide. With 35 million people over 60 years of age with dementia, there is an urgent need to develop new treatments for AD. To streamline this process, it is imperative to apply insights and learnings from past failures to future drug development programs. In the present work, we focus on how modeling and simulation tools can leverage open data to address drug development challenges in AD.

Mathematical Disease Progression Modeling in Type 2/3 Spinal Muscular Atrophy

INTRODUCTION

We propose a mathematical model to empirically describe spinal muscular atrophy (SMA) progression assessed by the 3 domains of the motor function measure (MFM) scale. The model implements development and deterioration of muscle function.

METHODS

Nonlinear mixed-effects modeling was applied to data from 2 observational studies and 1 prospective clinical efficacy study comprising 190 healthy participants and 277 patients with type 2/3 SMA.

RESULTS

The model evidenced correlations between parameter estimates for different MFM domains. Slower development in MFM domain D1 (standing and transfers) was associated with faster deterioration for MFM domains D2 (proximal and axial motricity) and D3 (distal motor function).

DISCUSSION

The model describes all individual data well, although sparseness and variability of observational data prevented numerically stable estimation of parameters. Treatment duration in clinical studies was too limited to determine a proper drug-effect model that could differentiate between symptomatic and disease modifying effects. Muscle Nerve 58: 528-535, 2018.

A systematic review of studies examining the rate of lung function decline in patients with cystic fibrosis

A systematic review was performed (i) to describe the reported overall rate of progression of CF lung disease quantified as FEV₁%predicted decline with age, (ii) to summarise identified influencing risk factors and (iii) to review methods used to analyse CF lung disease progression data. A search of publications providing FEV₁%predicted values over age was conducted in PUBMED and EMBASE. Baseline and rate of FEV₁%predicted decline were summarised overall and by identified risk factors. Thirty-nine studies were included and reported variable linear rates of lung function decline in patients with CF. The overall weighted mean FEV₁%predicted over age was graphically summarised and showed a nonlinear, time-variant decline of lung function. Compared to their peers, Pseudomonas aeruginosa infection and pancreatic insufficiency were most commonly associated with lower baseline and more rapid FEV₁%predicted declines respectively. Considering nonlinear models and drop-out in lung disease progression, analysis is lacking and more studies are warranted.

Kinetics of drug action in disease states: towards physiology-based pharmacodynamic (PBPD) models

Gerhard Levy started his investigations on the "Kinetics of Drug Action in Disease States" in the fall of 1980. The objective of his research was to study inter-individual variation in pharmacodynamics. To this end, theoretical concepts and experimental approaches were introduced, which enabled assessment of the changes in pharmacodynamics per se, while excluding or accounting for the cofounding effects of concomitant changes in pharmacokinetics. These concepts were applied in several studies. The results, which were published in 45 papers in the years 1984-1994, showed considerable variation in pharmacodynamics. These initial studies on kinetics of drug action in disease states triggered further experimental research on the relations between pharmacokinetics and pharmacodynamics. Together with the concepts in Levy's earlier publications "Kinetics of Pharmacologic Effects" (Clin Pharmacol Ther 7(3): 362-372, 1966) and "Kinetics of pharmacologic effects in man: the anticoagulant action of warfarin" (Clin Pharmacol Ther 10(1): 22-35, 1969), they form a significant impulse to the development of physiology-based pharmacodynamic (PBPD) modeling as novel discipline in the pharmaceutical sciences. This paper reviews Levy's research on the "Kinetics of Drug Action in Disease States". Next it addresses the significance of his research for the evolution of PBPD modeling as a scientific discipline. PBPD models contain specific expressions to characterize in a strictly quantitative manner processes on the causal path between exposure (in terms of concentration at the target site) and the drug effect (in terms of the change in biological function). Pertinent processes on the causal path are: (1) target site distribution, (2) target binding and activation and (3) transduction and homeostatic feedback.

Clinical pharmacology = disease progression + drug action

Clinical pharmacology is concerned with understanding how to use medicines to treat disease. Pharmacokinetics and pharmacodynamics have provided powerful methodologies for describing the time course of concentration and effect in individuals and in populations. This population approach may also be applied to describing the progression of disease and the action of drugs to change disease progress. Quantitative models for symptomatic and disease-modifying effects of drugs are valuable not only for describing drugs and diseases but also for identifying criteria to distinguish between types of drug actions, with implications for regulatory decisions and long-term patient care.