De-risking neurology drug development with better mouse models

What It Covers

Brandy Wilkinson, CEO of GEM PharmaTech, and neuroscience pipeline leader Ricky Feng explain how the company's library of over 25,000 genetically engineered mouse models addresses neurology's sub-10% clinical success rate by building preclinical tools that mirror human disease biology, biomarkers, and disease staging.

Key Questions Answered

• •Model selection risk: Reaching for convenient, overexpressing mouse models rather than disease-relevant ones is a primary driver of CNS drug failure. Less than 10% of CNS candidates entering Phase 1 ever reach market. Biotechs under budget and timeline pressure should prioritize model fit over speed, as mismatched models generate efficacy data that becomes irrelevant in the clinic.
• •Multi-pathology model design: GEM PharmaTech's F83T Alzheimer's model combines APP and PSEN1 mutations for amyloid pathology with a MAPT mutation for tau pathology, capturing neuroinflammation and memory decline simultaneously. Drug developers should demand models that replicate multiple co-occurring disease mechanisms rather than single-pathway constructs that miss the full clinical picture.
• •Disease staging alignment: Most preclinical studies treat animals before symptoms appear, while human trials enroll patients with established pathology. Drug developers should design studies across multiple disease stages and align animal intervention timing with the patient population their clinical trial will actually enroll, or risk flattening efficacy signals entirely.
• •Blood-brain barrier humanization: Only 0.1% of injected biologic doses typically reach the brain. GEM PharmaTech's humanized transferrin receptor mouse model tests whether receptor-mediated transcytosis strategies actually achieve sufficient brain exposure before costly CSF or human imaging studies. Additional humanized models targeting CD98HC and IGF1R are in development to cover a wider range of delivery mechanisms.
• •Integrated biomarker readouts: Linking behavioral test results directly to molecular and pathological data from the same animal cohort strengthens go/no-go decisions. GEM PharmaTech collects CSF and blood samples post-behavior testing to measure the same diagnostic markers used in human trials, such as phosphorylated tau ratios, creating a dataset that speaks the same language as clinical endpoints.