Biostatistics. Reimagined.
We combine cutting-edge statistics, AI, and cloud technology for every stage of the pharmaceutical lifecycle - from trial design to post-market surveillance.
Services
Clinical Trials
Clinical trials use statistics to evaluate the safety and efficacy of new drugs. The most common statistical techniques in clinical trials include ANOVA (Analysis of Variance), logistic regression, survival analysis, and mixed models.
We offer AI-driven simulations and real-time dashboards to design more efficient trials, saving time and costs. We provide reports with statistical evidence tailored for FDA or EMA submission. We help clients use statistical techniques to modify trials dynamically based on interim results, enhancing flexibility.
Bioequivalence (BE)
Bioequivalence refers to the demonstration that a generic drug performs in the same way as the brand-name counterpart. The 90% Confidence Interval (CI), Two One-Sided Tests (TOST), and log-transformed ANOVA are commonly used to assess if the generic drug's pharmacokinetic properties are statistically equivalent to the reference drug.
We offer an intuitive platform for clients to design and simulate bioequivalence studies with automatic compliance checks. We use machine learning to predict the likelihood of BE study meeting regulatory standards,reducing the chance of failure. We generate regulatory-compliant BE reports in a fraction of the time using automated templates and analytics.
Pharmacokinetics (PK)
PK statistics analyze how the body absorbs, distributes, metabolizes, and excretes a drug. Common methods include Non-Compartmental Analysis (NCA) and compartmental modeling. Key parameters like AUC (Area Under the Curve), Cmax (maximum concentration), and half-life (t1/2) are used to evaluate drug exposure.
We develop tool that lets pharma simulate different dosing scenarios and observe PK behavior under varying conditions. We provide AI-powered predictions of how changes in dosage, frequency, and formulation will affect PK profiles. We offer also advanced modeling tools for analyzing PK data accross diverse populations using NONMEM or Monolix.
Pharmacodynamics (PD)
Pharmacodynamics deals with the effects of a drug on the body, particularly its mechanism of action. Statistical techniques like dose-response curves, Emax models, and nonlinear regression are used to characterize the relationship between drug concentration and therapeutic effect.
We provide simulation tools to model and predict drug efficacy under different conditions. We use machine learning to analyze historical data and predict optimal dosing regimens for maximum therapeutic effect. We help our clients design experiments that optimize dosing schedules based on clinical or preclinical data.
Population PK/PD
Population pharmacokinetics and pharmacodynamics use mixed-effects modeling to examine variability in drug responses across different populations. Tools like NONMEM or Monolix are used to model this variability and identify factors that impact drug performance.
We provide cloud-based tools for simulating drug behavior across different populations, allowing for more informed trial designs. We offer statistical consulting for analyzing real-world data to optimize dosing and efficacy for diverse patient groups. We use machine learning to identify patient subgroups that may have adverse reactions, guiding personalized medicine approaches.
Stability Studies
Stability studies assess how environmental factors affect the chemical stability and efficacy of drugs over time. Statistical methods, are used to model shelf-life and predict the drug's expiration date.
We develop software that predicts the stability of drugs over time based on real-time environmental data. We automate the generation of shelf-life estimates, improving efficiency and reducing human error. We provide consulting on best practices for conducting stability studies and interpreting data.
Manufacturing & Quality Assurance (QA)
In pharmaceutical manufacturing, Statistical Process Control (SPC) and Design of Experiments (DOE) are used to monitor and optimize the production process. These techniques ensure the consistent quality of drugs and minimize deviations.
We provide real-time dashboards for monitoring production processes, flagging any statistical outliers. We use AI to predict when a production line might deviate from standards, preventing costly errors. We offer automated tool to optimize manufacturing conditions, reducing waste and improving efficiency.
Regulatory Submissions
In regulatory submissions, pharma companies use statistical methods like meta-analysis, Bayesian statistics, and resampling to analyze trial data and strengthen their submissions to FDA or EMA.
We provide tools to automatically compile trial data into a regulatory-ready submission format. We offer meta-analysis services to synthesize data from multiple trials, strengthening the submission package. We provide Bayesian tools to model clinical trial data with uncertainty, aiding in complex regulatory submissions.
Post-market Surveillance
After a drug is released to the market, pharmacovigilance involves monitoring for adverse events. Statistical methods like signal detection, survival analysis, and time series analysis are used to identify potential safety issues.
We offer AI-based systems that can automatically detect safety signals from real-world data.
Blockchain for Audit & Integrity
Seal statistical reports, raw data, and protocol versions using blockchain for regulatory compliance and fraud prevention.
Blockchain ensures tamper-proof, transparent audit trails and automates compliance with cryptographic integrity. It builds regulatory trust, streamlines collaboration, and secures data across the clinical lifecycle
Why Vedrax?
We're a team of senior biostatisticians, engineers, and data scientists with deep pharma experience. We leverage the latest in AI, cloud infrastructure, and blockchain to make pharmaceutical development faster, safer, and smarter.
Ready to modernize your pharmaceutical pipeline?
Request a strategy session with one of our experts.