Central nervous system (CNS) drug development rarely fails for one reason. It often fails because critical uncertainties are carried too far into the development process. A compound may advance without convincing evidence of relevant CNS exposure. A clinically appropriate patient group may remain too heterogeneous biologically. A validated clinical scale may still not be sensitive enough or introduce variability. A protocol may appear robust on paper, yet prove difficult to recruit or execute consistently. These issues are often treated as operational problems during study conduct. In many cases, however, they originate much earlier, in the assumptions and decisions that shape the trial design.
The key question is whether the study is efficiently designed to answer the right development questions before the sponsor commits to the next stage. The goal is not simply to generate data, but to generate the evidence needed for more informed development decisions.
Better CNS trials start with earlier proof
In CNS development, early-phase studies should do more than generate safety and tolerability data. They should help determine whether a compound has a credible path forward by addressing dose-relevant CNS exposure, target engagement and proof-of-mechanism where possible.
Depending on the program, this may involve healthy volunteer studies, small patient cohorts or hybrid early-phase models that combine both. It may also involve translational biomarkers that can further strengthen the strategy by linking drug exposure and pharmacology to a measurable biological effect.
We help sponsors use early-phase design to ask sharper questions sooner, before large efficacy trials start - not only “is the drug safe enough to continue?” but also “is there enough evidence to justify the next investment?”
Biomarkers find early signals
In Phase 1 CNS trials, biomarkers are primarily used to reduce uncertainty around a compound. They can help determine whether a drug reaches the CNS at relevant concentrations, engages its intended target and produces the expected downstream biological effect.
Depending on the mechanism of action, evidence may be generated through measurements of drug exposure in plasma and cerebrospinal fluid (CSF), receptor occupancy using positron emission tomography (PET), electrophysiological or neuroimaging readouts, fluid-based pharmacodynamic markers and responses to functional challenge paradigms. The most useful biomarkers are those closely linked to the mechanism of action and interpretable alongside pharmacokinetics (PK), dose and target engagement.
These data can provide early proof-of-mechanism, inform dose and regimen selection, and show whether the biological assumptions underlying the program hold true in humans. For sponsors, the value is clear: stronger evidence that the compound is reaching the right place, affecting the intended pathway, and has a credible basis for progressing into patient studies or larger efficacy trials.
CSF-based assessments can strengthen early CNS evidence
For some CNS programs, plasma data alone may not provide sufficient insight into drug activity within the central nervous system. CSF data however can generate more direct evidence of CNS exposure and pharmacodynamic response. It can help sponsors assess CNS penetration, target engagement and pharmacokinetics/pharmacodynamics (PK/PD) relationships earlier in development.
The value of CSF sampling lies not only in sample collection, but in the quality of the evidence it generates. Successful implementation requires appropriate clinical setting, trained teams, careful participant management, protocol discipline, and experience with complex early-phase procedures. That is why we position CSF sampling as a strategic early-phase capability. When used strategically, CSF data can help reduce uncertainty, strengthen early development decisions, and provide greater confidence before advancing into larger studies.
Recruitment problems are often design problems
Patient recruitment is often discussed too late, as if it begins once the sites have been selected. In reality, recruitment risk often originates much earlier during study design. Narrow eligibility criteria, high visit burden, complex assessments and unrealistic operational assumptions can make scientifically sound studies difficult to recruit, despite the involvement of experienced clinical trial sites. In CNS research, these challenges are further amplified by vulnerable populations, caregiver requirements and specialized procedures, making protocol feasibility a critical determinant of recruitment success.
We proactively address recruitment risks during study design to support feasible protocols, reduce the risk of operational challenges and protect study timelines and budgets. This includes assessing whether the target population is accessible, whether study assessments reflect clinical reality, whether study requirements and procedural burden are aligned with the needs and expectations of patients and caregivers and whether participating sites have the capabilities and infrastructure required to successfully conduct the study. By identifying potential barriers early, sponsors can make more informed design decisions before recruitment risks become enrollment delays.
Through our CNS site network and partner sites across Europe and North America, we collaborate with specialized neurology centers, hospital-based research facilities, dedicated clinical research units and investigators experienced in complex CNS studies. This supports access to relevant patient populations while ensuring study requirements are evaluated by teams familiar with the practical realities of CNS trial conduct.
Data quality depends on consistent CNS trial execution
In CNS clinical trials, execution quality can directly influence the reliability and interpretability of study data. Assessments such as electroencephalography (EEG), cognitive testing, clinical scales and functional testing are particularly sensitive to variability. Small differences in assessment procedures, rater performance or site execution can make it more difficult to distinguish treatment effect from background noise. That is why CNS trials require standardized procedures, experienced and well-trained teams, robust study monitoring and strong site oversight. Close collaboration between clinical operations, trial sites and biometrics teams is essential to understand the downstream impact of data variability, identify potential sources of variability and support data quality throughout the study.
Our integrated model brings together early-phase clinical operations, CNS-capable sites, laboratory support and biometrics expertise. By aligning trial conduct, data collection and analysis throughout study planning and conduct, we help reduce operational fragmentation and handover risk, strengthen data consistency and support more confident development decisions.
The difference is not scale alone
CNS programs often struggle when critical assumptions about the compound, patient population or study design are carried forward without being adequately investigated. As drug development progresses, these uncertainties can become increasingly difficult and costly to address.
Our approach is to work with sponsors to identify and challenge key development risks early, before they have the opportunity to scale. For example, whether the mechanism of action has been sufficiently characterized, whether biomarkers can support evidence-based decision-making, whether the patient population is biologically and operationally appropriate and whether the protocol is realistic for target population and operationally feasible for trial sites to execute.
By addressing these questions early, sponsors can generate stronger evidence before progressing into larger and more resource-intensive studies. This can support earlier and more confident go/no-go decisions, more realistic development timelines, fewer avoidable protocol amendments, better management of recruitment risks, and stronger confidence when advancing to the next stage of development.
In summary, CNS development will always involve uncertainty, but many of the factors that ultimately impact study success or failure can be identified and addressed early in development, provided the study is designed to detect them. The earlier these signals are identified, the greater the opportunity to make informed decisions and allocate development resources effectively.
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