Description
- Abstract:
- Drug development within the pharmaceutical industry has historically proven to be a challenging feat. Investigational drug trials have a 90% failure rate due to adverse events, dosing, and the drug candidate itself (McKenzie, 2024). When evaluating drug development success in the central nervous system (CNS) space specifically, this rate increases with the majority of failures landing in the mid to late stages of development (McKenzie, 2024). In fact, “one study found that between 1995 and 2007, CNS drugs achieved FDA approval at less than half the rate of non-CNS drugs” (McKenzie, 2024). Part of this problem stems from the fact that the mechanisms behind neurological disorders are poorly understood (NCBI, 2014). Neuropharmaceutical companies face challenges with identifying and validating targets, understanding mechanisms of disease, predictive modeling, utilizing biomarkers, identifying clear regulatory pathways, and relying on and reproducing published data during pre-clinical development (Pankevich, 2014). This poses challenges for translational neuroscience research that aims to use fundamental neuroscience concepts for clinical applications and drug development. Recent, novel solutions to existing treatment challenges have included use of electroencephalography (EEG) and computational modeling. Electroencephalography (EEG) is a technique used to record the brain’s electrical activity which results in a printout known as an electroencephalogram (EEG) (Mayo Clinic, 2025). Electroencephalograms (EEG) are valuable for neurological disease treatment because they are a non-invasive method for directly measuring brain electrical activity and detecting normal and abnormal brain wave patterns (Figure 1). EEG can identify the location and type of abnormal brain activity, which is crucial for pinpointing the source of neurological symptoms and developing targeted treatment strategies (Mayo Clinic, 2025). The Jones lab at Brown University has developed a software, known as Human Neocortical Neursolver (HNN), that utilizes EEG signatures and EEG biomarkers to simulate a biophysical model of a cortical column which can be used to study the cell and circuit generators of these biomarkers in neurological disorders. Through an ongoing proof-of-concept project funded by the Brown Innovations to Impact Award, a healthy state EEG and a disease-state EEG for schizophrenia and Fragile X disorder will be simulated while the effects of a mechanistic, circuit-level based treatment will be evaluated to determine if an administered pharmaceutical treatment brings the patient’s disease state closer to the healthy state. In doing so, the HNN can be used to help with lead selection and dosing of drug candidates. This thesis report aims to highlight all current proof-of-concept progress and map out a complete commercialization plan to employ the HNN as a tool to help decision making in neuropharmaceutical drug development.
- Notes:
- Thesis (Sc. M.)--Brown University, 2025
Citation
Hohil, Adrianna Adela,
"Biophysical Simulations for Effective Neuropharmacology Treatment: A Market Analysis and Commercialization Strategy for the Human Neocortical Neurosolver"
(2025).
Biotechnology, Biology and Medicine Theses and Dissertations.
Brown Digital Repository. Brown University Library.
https://repository.library.brown.edu/studio/item/bdr:gh6kh9u2/
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