How Patient-Derived Models Are Empowering Neurodegenerative Disease Drug Discovery

Neurodegenerative diseases affect millions of people worldwide, with the burden of these diseases expected to double over the next two decades. 

An umbrella term for conditions that are characterized by slow damage or degradation of parts of the nervous system, neurodegenerative diseases include Alzheimer’s disease, Parkinson’s disease, Lewy body dementia, multiple sclerosis and amyotrophic lateral sclerosis (ALS).

Currently, there are few therapeutic options for neurodegenerative diseases, with discovery efforts challenged by diverse disease mechanisms, multiple triggering factors and the use of models that have limited translatability. 

Patient-derived models of neurodegenerative diseases 

Human induced pluripotent stem cell (iPSC)-derived models using cells derived from patients could address the translatability challenge and improve drug discovery, as they carry genetic mutations and unique properties from each individual, building a bigger picture of the variety within neurodegenerative disease. 

To create these disease models, somatic cells are taken from patients with neurodegenerative diseases and induced into iPSCs by pluripotency factors. From there, the cells can be differentiated into the cell types implicated in disease onset.

Multiple different neuronal cell types can be derived from iPSCs, including neurons, astrocytes, microglia and oligodendrocytes. With this capability, three-dimensional organoids can be created to reflect neurodegenerative disease pathology more accurately.

“By using patient-derived models, this means putting the patient at the centre of drug discovery. It means the potential new therapeutics are tested in relevant patient material during drug discovery, enhancing the likelihood of successful translation from the lab to the clinic,” Heather Mortiboys, professor of cellular neuroscience and metabolism at The University of Sheffield, told Technology Networks. 

Her research has identified that in some conditions, such as Parkinson’s disease, the response to therapeutics in patient-derived models was consistent across cell lines, even between those with genetic causes of Parkinson’s and those without. 

However, “when investigating in other conditions, the response to therapeutics varies between patients, particularly between those with and without genetic causes,” Mortiboys explained. 

Supporting drug discovery throughout the pipeline 

Patient-derived models can be used throughout the drug discovery pipeline, from identifying molecular mechanisms of disease and potential drug targets to high-throughput screening of drug candidates.

“These patient-derived models have the possibility to intertwine at multiple stages of drug discovery, even at the high-throughput screening level, to lead optimization and even in clinical trials with the sampling of patients at the start of a clinical trial,” Mortiboys said. 

However, patient-derived models face challenges when being scaled for industry adoption. “The samples are a precious resource which need to be handled with care and consideration,” said Mortiboys. By standardizing how these samples are handled and imposing very strict conditions on their use, it becomes possible to use them in large-scale drug screening.

Additionally, “challenges remain in obtaining an even wider range of patient-derived samples for various conditions,” Mortiboys noted. 

Looking to the future of neurodegenerative disease drug discovery 

Recent advances in biomarkers – such as the discovery of blood-based tau and the Aβ42/40 ratio for Alzheimer’s disease, and neurofilament light chain for ALS and Parkinson’s disease – hold promise for earlier detection as well as assessing the progression of disease, and for analyzing the effectiveness of new therapeutics. 

“We are at an exciting time in neurodegenerative disease drug discovery.” 

“New therapeutics being tested now are being tested in more sophisticated clinical trials, set up to be able to assess disease modifying compounds,” Mortiboys said. “This along with advances in our understanding of the disease mechanisms, advancements in drug discovery, mean this is an exciting time poised to translate the promising lab discoveries into the clinic for real patient benefit.” 

Mortiboys will be presenting at ELRIG Drug Discovery 2025, focusing on data from patient-derived models that has been generated over a number of years and how this data can be used to maximize the adoption of patient-derived models for drug discovery.

Prof. Heather Mortiboys was speaking to Katie Brighton, Science and Newsletter Writer for Technology Networks.