# Drug discovery and development for Parkinson’s disease: are preclinical models good enough?

## TL;DR

This paper reviews how well preclinical models for Parkinson’s disease capture human disease complexity and identifies challenges in drug discovery.

## Contribution

The paper provides a critical evaluation of preclinical models for Parkinson’s disease, highlighting their limitations and suggesting improvements for translational research.

## Key findings

- Current preclinical models fail to fully replicate the heterogeneity and complexity of human Parkinson’s disease.
- Non-motor symptoms of PD are often underrepresented in preclinical models.
- Translational gaps persist due to limitations in modeling both motor and non-motor features of PD.

## Abstract

Parkinson’s disease (PD) remains a major challenge for translational neuroscience, with an increasing global prevalence and persistent unmet therapeutic needs. While its classical motor symptoms, such as bradykinesia, rigidity, and tremor, are well characterized, the clinical spectrum extends to diverse and often disabling non-motor manifestations, including hyposmia, constipation, and sleep disturbances. These features typically precede motor deficits and may dominate the late stages of disease. Despite decades of research, existing treatments remain primarily symptomatic and fail to halt disease progression. This situation has driven the development of a broad repertoire of preclinical models—ranging from in vitro cellular systems to complex animal models—to better understand pathogenesis and identify disease-modifying strategies. However, significant translational gaps persist, partly due to limitations in how well these models recapitulate the heterogeneity and complexity of human PD. In this review, we critically examine the main preclinical models available for PD, assessing their strengths and weaknesses for modeling both motor and non-motor features. We discuss recent advances, persistent challenges, and highlight key considerations for improving the predictive value of experimental models in drug discovery for Parkinson’s disease.