Clinical Development

How many subjects do I need in my clinical trial to meet the requirements of regulators?

What trial design will best support my development strategy?

What is the most efficient way to satisfy the regulatory requirements for long term safety data?

What do I need to do to ensure the quality of my clinical trial data will pass regulatory standards?

What are ADAM and STDM datasets and why do I need them?

Do I really need a statistical analysis plan?

What’s an ISS? How is it different from an ISE? How do I know if I need one?

How can I address pharmacogenomics, pharmacokinetics or pharmacodynamics in my clinical development plan?

What efficacy and safety endpoints in my phase 3 clinical trials will satisfy the requirements for a regulatory submission?

Example of solutions to address the above questions.

Evaluate products (drugs or devices) in relation to the current global scientific and medical landscape.

Provide strategic technical, medical and pharmacovigilance input for development plans, clinical trial designs, study documents, submissions, publications and company literature.

Interact with regulatory authorities or prepare in-house teams for presentations, discussions and interactions with regulatory authorities.

Manage vendors.

Ensure datasets intended for submission comply with the CDISC (Clinical Data Interchange Standards Consortium) standards required by regulators.

Produce or input to the full range of clinical and regulatory documents (protocols, statistical analysis and data management plans, CRF/eCRF, CCDS, CSR, IB, HA briefing and response documents, CTA, CTD overviews and summaries, supplements, prescribing information, risk management plans, PSUR, DSUR).

Classify adverse events using MeDRA coding.

Analyze and interpret study data.

Provide medical monitoring / site liaison services.

Perform SAE reconciliation, reporting and procedures.

Execute pharmacovigilance Quality Assurance including SOP development>

Manage DSMB.

Create, review, QC and update study documentation.

Review data management and statistical plans.

Review data outputs for quality issues.