Early Decisions That Shape Success In CNS Drug Development And Commercialization, Part 2

A conversation with Ginger Johnson, SVP, Neuroscience Lead, Lumanity

If you haven't had a chance yet, be sure to read part 1 of this two-part interview series. In part 2, Ginger continues to provide insights for R&D teams on: moving from preclinical to clinical, endpoint and study design challenges, biomarkers supporting treatment decisions, and her ultimate piece of advice on starting a CNS pipeline.

What does translation typically look like from preclinical to clinical with CNS drug candidates? What should R&D keep in mind?

Translation from preclinical to clinical development in CNS is not simply a question of whether the science is sound. It is also a question of proof and clinical relevance. A mechanism may work in a model, but early clinical development must determine whether the therapy reaches and affects the intended target in humans at a plausible dose and whether those biological effects have the potential to translate into meaningful clinical benefit.

Preclinical models can be essential for understanding mechanisms, shaping dose hypotheses, and establishing biological plausibility. However, in many CNS settings, they provide an indirect representation of human disease and may have limited ability to predict clinical outcomes. This is particularly challenging in psychiatry, pain, and slowly progressive neurodegenerative diseases, where behavior, function, disease progression, and placebo response can be difficult to model.

My advice to R&D teams would therefore be to focus early development on generating decision-relevant evidence, rather than collecting data simply because it is scientifically interesting. Several questions are particularly important:

• Does the drug reach the relevant tissue and achieve adequate exposure, such as sufficient penetration into the brain or delivery to the intended cells?
• Is there evidence of target engagement or pathway modulation in humans, meaning that the drug is affecting its intended biological target or producing the expected downstream effect?
• Which patient subgroup is most likely to respond and how will those patients be identified?
• What early signal would suggest the potential for clinically meaningful benefit, rather than simply showing that a biological measure has changed?

The type of proof required will vary by disease and modality:

• CNS small molecules: Brain exposure, target engagement, and pharmacodynamic effects may be central to establishing early translational confidence.
• Antisense oligonucleotides and other gene-targeted therapies, particularly in orphan neurodegenerative diseases: Delivery to the relevant tissue, durability of effect, biomarker change, and selection of the appropriate patient population may be critical.
• Psychedelics: Mechanistic measures, such as EEG or imaging, may help characterize pharmacological effects, but they should be tied to a specific development decision, such as dose selection, patient selection, or a hypothesis about the durability of clinical benefit.

The most valuable early evidence is that which reduces a specific uncertainty and supports the next development decision. Finally, a pragmatic reality in CNS is that early evidence often cannot prove efficacy cleanly as it may only predict it broadly. Prioritization therefore becomes even more important. The goal of early clinical work should be to reduce the biggest uncertainties fast, using endpoints and biomarkers that clearly inform the next development decision, rather than collecting data simply because it’s novel or feasible.

Are there particular endpoint or study design challenges in neuroscience that companies consistently underestimate early in development?

Yes, and this is something I have seen repeatedly. A recent amyotrophic lateral sclerosis (ALS) example illustrates the risk: a therapy reached the market based on encouraging earlier evidence, but a larger confirmatory study failed to demonstrate a statistically significant effect on functional outcomes. The most common problem occurs when organizations assume that a statistically significant result in one study, or a change in a biological measure, will automatically translate into meaningful and reproducible benefit in practice. In CNS, the relationship between a measured change and a patient-relevant outcome can be nuanced. A scale or biomarker may move, but clinicians, patients, caregivers, and payers will still ask:

• What changed in the patient’s daily life?
• Did cognition, function, or quality of life improve?
• Was the benefit durable?
• Did caregiver burden decline?
• Did the treatment reduce relapse, healthcare utilization, or disease progression?

This is especially important in neurodegeneration, where cognition, function, and disease progression may move slowly and may require longer time horizons. For example, in Alzheimer’s, a biomarker effect needs to connect back to cognition, function, safety, and caregiver impact, and in PSP or Huntington’s disease, endpoint selection is tightly tied to natural history and the expected rate of progression.

In psychiatry and pain, the challenge is different but equally as important: placebo response, site variability, and heterogeneity can make detecting true effects difficult. In psychedelic programs for treatment-resistant depression (TRD), a short-term symptom improvement may be visible, but endpoints and follow-up are needed to support a credible story around durability and functional recovery.

Teams can also underestimate the importance of secondary outcomes: function, durability, and caregiver burden. Real-world feasibility is sometimes treated as “nice to have,” but in neuroscience especially, it often becomes central to whether a program is believable to clinicians and payers.

I would say that a useful test and question that needs to be considered early on is: will this endpoint change how someone treats a patient and can it support a claim that travels beyond the trial?

How should teams use biomarkers to guide trial design and patient selection, enable diagnosis and patient finding in the real world, and support treatment decisions? Where do companies most often overreach?

Biomarkers can be extremely valuable in CNS, but only when their role is clearly defined: they can support trial design, patient selection, diagnosis and patient findings and treatment decisions, provided they are used for the purpose they have been validated to fulfil. In practice, biomarkers can play several distinct roles, including:

• trial design and enrichment
• confirming target engagement or pathway activity
• patient identification and selection
• monitoring disease biology
• supporting diagnosis and patient finding in the real world
• informing treatment decisions.

The key is that the evidentiary bar is different for each role. A biomarker that helps enrich a Phase 2 trial does not automatically translate into a routine clinical test. A biomarker that increases biological confidence does not automatically substitute for clinical benefit and a biomarker that is useful in a specialty center may not be scalable across real-world care.

Regulatory relevance is also context dependent. In some settings, biomarkers can support regulatory decision-making, including by serving as surrogate endpoints, but that does not make them broadly validated clinical endpoints across CNS. Alzheimer’s disease is one of the clearest examples: amyloid biomarkers now play a central role in diagnosis, patient selection, drug development, and regulatory review, particularly for anti-amyloid antibodies. Even so, a reduction in amyloid burden is not sufficient on its own. It must be linked to outcomes that matter in practice, including cognition, function, safety, appropriate patient selection, and the feasibility of delivering and monitoring treatment.

Across much of CNS, including psychiatry, Parkinson’s disease, pain, and many orphan neurodegenerative diseases, biomarkers are most often best used to strengthen trial design and biological confidence, not stand-alone substitutes for clinical benefit.

Companies most often overreach in the following two areas:

1. Overestimating maturity (assuming that an exploratory biomarker will quickly become a validated clinical decision tool).
2. Underestimating real-world implementation (designing a biomarker-dependent therapy without a realistic plan for testing access, clinical workflow, reimbursement, and patient finding).

In psychiatry, EEG, imaging, and digital measures may help with mechanistic confidence or enrichment, but they are rarely ready to guide prescribing. In genetic or orphan neurodegenerative diseases, biomarkers and genetics can be central to patient finding, but teams still need to plan for referral pathways, specialty-center concentration, and operational burden.

I would also encourage organizations to remember a few simple rules of thumb: don’t ask a biomarker to do a job it hasn’t been proved to do and don’t build a strategy that depends on a test the real world cannot reliably deliver.

What is the single most useful piece of advice you would give a company starting a CNS pipeline today to maximize real-world patient impact?

The single most useful piece of advice I would give a biotech or biopharma company starting a CNS pipeline today is to design with eventual patient use in mind while the science and development plan are still taking shape. Before the pivotal development path is locked, my advice would be to describe in plain terms how the therapy would be used in practice:

• Who is the patient and how will they be identified and diagnosed?
• Where will they be treated and by whom?
• What outcome will make a meaningful difference in their daily life?
• What burden will treatment place on patients, caregivers, and the healthcare system, including testing, monitoring, and visits?
• What evidence would be needed to change clinical practice?
• What evidence will build confidence with regulators, clinicians, payers, and patients?

If the therapy will depend on a biomarker, specialized diagnostic capability, referral network, or complex delivery model, those requirements should shape discovery and early development. Companies may have years to help build the necessary infrastructure, but they need to understand those dependencies early so that the modality, biomarkers, endpoints, trial design, and the development path remain aligned with eventual patient use.

The next step is to work backward from that future treatment scenario to the evidence needed during development. CNS programs can generate many biologically interesting signals, but the most valuable early evidence informs a concrete decision: which patients to study, what dose to advance, whether the target is being affected as intended, whether the effect is likely to be clinically meaningful, and whether the therapy can ultimately be delivered and monitored in the intended care setting.

About The Expert

Ginger Johnson, Ph.D., senior vice president and neuroscience lead at Lumanity, advises biopharmaceutical companies on asset, portfolio, and development strategy across neurology, psychiatry, and pain. She brings more than 25 years of experience spanning neuroscience research, biotechnology, life sciences investing, and strategic consulting. Her work focuses on connecting scientific innovation with the clinical, regulatory, payer and commercial evidence needed to translate promising CNS therapies into meaningful patient impact. Ginger has held leadership roles at the National Institute of Mental Health, Northwestern University’s Center for Biotechnology and Chase Capital Partners/CCMP.