Driving insights into neurological diseases through data

2 minute read


Date: 30 May 2024


Author: Chiara Banas, PhD

Contributors: Hannah Gaimster, PhD, Amanda White



Gathering sufficient data to drive new insights on drug targets and therapies poses a significant hurdle in addressing human illnesses. Nowhere is this challenge more evident than in neurological disorders like Alzheimer's, Parkinson's, and multiple sclerosis.

Traditionally, treatments for these conditions have focused on managing the symptoms rather than treating the disease. Further, the complex nature of neurological diseases complicates the development and approval of novel drugs, making it challenging for them to pass clinical trials successfully.

Therefore, it becomes critical to support translational research by reducing barriers to accessing medical data, thereby fostering the discovery of new therapeutic drug targets for neurological diseases.


Lack of Data Slows Down Progress in Finding Successful Treatments for Neurological Diseases

Despite significant advancements in medical technology and research methodologies, a critical obstacle persists: the lack of available and comprehensive data. Use of data is critical in overcoming this obstacle as it has been estimated that research insights can grow as much as 100x when data is increased by 10-fold.

Recent advancements have been made in the development of drugs for Alzheimer’s. In 2021, the FDA granted approval for Aducanumab, followed by Lecanemab in 2023, under the accelerated approval pathway. Both Aducanumab and Lecanemab are antibody-based drugs targeting the amyloid protein, a key marker for the disease. By inhibiting the formation of amyloid plaques around neurons, they work to slow down the progression and symptoms of Alzheimer’s.


Nevertheless, in the preceding two decades leading up to the approval of these drugs, there had been no new medications approved for Alzheimer’s.


Why Has It Been So Difficult to Approve More Treatments?

  • Complexity of the Disease: Alzheimer’s disease is multifaceted, involving a myriad of pathological processes, including beta-amyloid plaque formation, tau protein tangles, neuroinflammation, and neuronal loss. Deciphering the complicated interplay of these mechanisms and their role in disease progression has proven to be challenging.

  • Limited Understanding of Disease Mechanisms: While scientists have made significant strides in elucidating the underlying mechanisms of Alzheimer’s, many aspects of the disease still remain elusive. Enhancing researchers’ access to data and connectivity could greatly facilitate our comprehension of the disease.

  • High Failure Rate in Clinical Trials: Developing drugs for Alzheimer’s disease is notoriously difficult, with a staggering failure rate in clinical trials. Many promising candidates have faltered in late-stage trials, often due to lack of efficacy or unexpected side effects. The complexity of the disease and the limitations of current animal models pose significant hurdles in accurately predicting drug responses in human patients.

  • Diagnostic Challenges: Accurately diagnosing Alzheimer’s disease in its early stages remains a significant challenge. Clinical trials often enroll patients with varying degrees of cognitive impairment, leading to heterogeneity in study populations and potentially confounding results. Improved diagnostic tools and biomarkers are needed to identify patients most likely to benefit from experimental treatments.


There Is a Need to Connect Real World Data to Fuel Disease Understanding, Drug Discovery, and Clinical Trial Approval

Despite these challenges, the urgency of finding effective treatments for Alzheimer’s disease has never been greater. With the aging population, the need for innovative therapies has never been more pressing. Collaborative efforts between academia, industry, and government agencies are essential to overcoming the obstacles against progress in Alzheimer’s disease.



Streamlining access to fragmented data for researchers could facilitate the identification of new targets, thereby enhancing the likelihood of success. Additionally, identifying suitable cohorts for clinical trials is paramount in advancing research efforts in neurology.


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