Clinical Scorecard: Building an Integrated Discovery Engine

At a Glance

Key Highlights

• Focus on early, integrated decision-making to reduce risk
• Strengthening capabilities in spatial biology for better target validation
• Investment in neuroscience to improve predictive power of preclinical models
• Utilization of advanced modalities like peptides and antibody-drug conjugates
• Integration of AI and machine learning for data analysis and decision-making

Guideline-Based Recommendations

Diagnosis

• Utilize spatial biology to connect molecular signals with disease biology
• Benchmark experimental models against human disease architecture

Management

• Adopt an integrated approach across disciplines for drug discovery
• Incorporate advanced data visualization for decision-making

Monitoring & Follow-up

• Track progress through integrated multiomics and translational biomarkers

Risks

• Address high clinical attrition rates in neuroscience
• Mitigate risks through early decision-making and integrated capabilities

Patient & Prescribing Data

Patients requiring new therapeutic modalities

Focus on high-value modalities and integrated approaches to improve outcomes

Clinical Best Practices

• Embed high-quality CMC thinking from the outset
• Leverage cross-disciplinary alignment for complex programs
• Utilize human-relevant methodologies for predictive discovery

References