Neuroscience drug development is undergoing a fundamental shift. After decades of setbacks, the approvals of lecanemab and donanemab mark the arrival of true disease-modifying therapies (DMTs) for Alzheimer’s disease [1,2]. At the same time, advances in Model-Informed Drug Development (MIDD), adaptive trial designs, and digital health technologies are reshaping how biotech companies design, test, and bring neurological drugs to market [3-5].
Neuroscience Drug Development: Momentum in Alzheimer’s, Parkinson’s, and Multiple Sclerosis
Alzheimer’s Disease. With neuroscience drug development a focal point in biotech, the Alzheimer’s, Parkinson’s, and Multiple Sclerosis pipelines are filled with promising candidates, driven by innovative approaches such as disease-modifying therapies (DMTs). With 182 active clinical trials in 2025 (up from 164 in 2024), the Alzheimer’s pipeline is dominated by DMTs (Dimethyltryptamine) [6,7]. FDA approvals of lecanemab (2023) and donanemab (2024) validated amyloid as a disease-modifying target [1,2], while Aducanumab’s withdrawal underscored the risks of weak biomarker-surrogate correlations [8,9].
Parkinson’s Disease. Although no DMTs are approved yet, 139 therapies are in development for Parkinson’s Disease, nearly half targeting disease modification [10]. Programs aimed at SNCA, LRRK2, and GBA are converging on shared pathways in inflammation, autophagy, and mitochondrial function [11-13].
Multiple Sclerosis (MS). With 20 approved multiple sclerosis treatments, MS is the most advanced neurological model [14]. Recent agents like ocrelizumab and siponimod highlight both precision targeting and long-term disease control [15,16], setting a precedent for what’s possible in Alzheimer’s and Parkinson’s.
Model-Informed Drug Development (MIDD) as a Core Driver
Alzheimer’s Disease: Both lecanemab and donanemab leveraged exposure–response models and amyloid PET imaging as surrogate endpoints to predict clinical benefit [3,4]. FDA’s approval of lecanemab hinged on integrated models linking PK predictions of brain exposure, exposure–response to cognition, and safety modeling for amyloid-related imaging abnormalities (ARIA) [5].
Parkinson’s Disease: Quantitative Systems Pharmacology (QSP) models are shaping how trials are designed. In LRRK2 inhibitor programs, QSP has enabled biomarker identification, dose optimization in genetically defined populations, and adaptive enrollment criteria [12,17].
Multiple Sclerosis: Machine learning models predicting cladribine response have achieved >80% accuracy [18].
Read more about: The Role of Clinical Pharmacology in New Drug Development
Beyond MIDD: Enhancing Neuroscience Clinical Trials with Complementary Strategies
Adaptive trial designs are increasingly adopted in neuroscience clinical trials, accelerating go/no-go decisions and reducing exposure to ineffective treatments [19].
Digital biomarkers (wearables, speech analytics, passive monitoring) provide continuous, high-resolution data and improve trial sensitivity [21,22].
Multi-target approaches are gaining traction after repeated failures of single-target programs [23].
Strategic Outlook
The next decade will reward companies that:
- Invest in MIDD capabilities (PK/PD, QSP, ML).
- Build digital endpoints into trial design early.
- Design for adaptivity (Bayesian frameworks, interim analyses).
- Collaborate with regulators, academia, and patient groups to access shared models and validated biomarkers.
For biotech sponsors, the message is clear: integrating MIDD, adaptive designs, RWE, and digital biomarkers is no longer optional—it’s the competitive baseline.
References
1. Dyck CH van, Swanson CJ, Aisen P, others. Lecanemab in early alzheimer’s disease. New England Journal of Medicine. 2023;388(1):9–21.doi:10.1056/NEJMoa2212948
2. Sims JR, Zimmer JA, Evans CD, others. Donanemab in early symptomatic alzheimer disease. JAMA. 2024;331(2):138–51.doi:10.1001/jama.2023.28340
3. Liu C, Luo W, McDade E, others. Population PK/PD analyses of lecanemab. CPT: Pharmacometrics & Systems Pharmacology. 2023;12(1):70–81.doi:10.1002/psp4.12862
4. Gueorguieva I, Farlow M, Ward R, others. Donanemab exposure–response in early alzheimer’s disease. Clinical and Translational Science. 2024;17(9):e12250.doi:10.1111/cts.12250
5. U.S. Food and Drug Administration. Clinical pharmacology review of lecanemab (leqembi). NDA 761269 [Internet]. 2023. Available from: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2023/761269Orig1s000ClinPharmR.pdf
6. Cummings J, Zhou Y, Lee G, others. Alzheimer’s disease drug development pipeline: 2025. Alzheimer’s & Dementia: Translational Research & Clinical Interventions. 2025;11:e70098.doi:10.1002/trc2.70098
7. Cummings J, Zhou Y, Lee G, others. Alzheimer’s disease drug development pipeline: 2024. Alzheimer’s & Dementia: Translational Research & Clinical Interventions. 2024;10:e12465.doi:10.1002/trc2.12465
8. Knopman DS, Jones DT, Greicius MD. Failure to demonstrate efficacy of aducanumab: An analysis of the EMERGE and ENGAGE trials. Alzheimer’s & Dementia. 2021;17(4):696–701.doi:10.1002/alz.12291
9. Howard R, Liu KY. Questions EMERGE as biogen claims aducanumab turnaround. Nature Reviews Neurology. 2020;16(2):63–64.doi:10.1038/s41582-019-0295-9
10. McFarthing K, others. Parkinson’s disease drug therapies in the clinical trial pipeline. npj Parkinson’s Disease. 2023;9:65.doi:10.1038/s41531-023-00522-7
11. Pagano G, Polychronis S, Wilson H, others. Therapeutic targets in parkinson’s: Insights from genetics. Lancet Neurology. 2020;19(10):830–40.doi:10.1016/S1474-4422(20)30214-5
12. Healy DG, Falchi M, O’Sullivan SS, others. LRRK2-associated parkinson’s disease. Lancet Neurology. 2008;7(7):583–90.doi:10.1016/S1474-4422(08)70117-0
13. Guglielmi L, Varrone A. GBA-associated parkinson’s disease mechanisms. Movement Disorders. 2022;37(2):227–39.doi:10.1002/mds.28836
14. Hauser SL, Cree BAC. Treatment of multiple sclerosis: A review. American Journal of Medicine. 2020;133(12):1380–1390.e2.doi:10.1016/j.amjmed.2020.05.049
15. Lamb Y. Ocrelizumab: A review in multiple sclerosis. Drugs. 2022;82(7):779–92.doi:10.1007/s40265-022-01765-0
16. Scott LJ. Siponimod: A review in SPMS. CNS Drugs. 2020;34:825–35.doi:10.1007/s40263-020-00771-z
17. Denaro C, Stephenson D, Müller MLTM, others. QSP frameworks in parkinson’s. Frontiers in Systems Biology. 2024;4:1351555.doi:10.3389/fsysb.2024.1351555
18. Menzin J, Nichols C, Louie M, others. ML prediction of cladribine response in MS. Multiple Sclerosis and Related Disorders. 2022;58:103509.doi:10.1016/j.msard.2022.103509
19. Ventz S, Alexander BM, Parmigiani G, others. Adaptive trial designs: A review. Journal of Clinical Oncology. 2017;35(6):604–11.doi:10.1200/JCO.2016.69.3237
20. Hatswell AJ, Baio G, Berlin J, others. Regulatory approval without RCT evidence. BMJ Open. 2016;6(6):e011666.doi:10.1136/bmjopen-2016-011666
21. Dorsey E, Omberg L, Waddell E, others. Deep phenotyping of parkinson’s with smartphones. Nature Biotechnology. 2020;38(3):310–6.doi:10.1038/s41587-019-0377-1
22. Lipsmeier F, Taylor K, Kilchenmann T, others. Smartphone testing in PD trials. Movement Disorders. 2018;33(8):1287–97.doi:10.1002/mds.27376
23. Bartsch T, Lenz T, Oertel W. Lessons from failed AD/PD drug trials. Lancet Neurology. 2023;22(2):99–111.doi:10.1016/S1474-4422(22)00426-0
24. Arrowsmith J, Miller P. Phase II/III attrition rates. Nature Reviews Drug Discovery. 2013;12(8):569. doi:10.1038/nrd4090
Share This
