Predict Outputs and Outcomes Before Spending Months and Millions
FORMsightAI enables AAV gene therapy developers to predict manufacturing output of proposed bioreactor conditions and optimize them in silico leading to precious time and cost savings.
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Predict the outcomes of modifying reactor conditions on variables like cell titer and empty/fill capsid ratios. This predictive approach aids in informed decision making for optimizing bioreactor runs.
Utilize simulations to analyze the effects of altering triple transfection molar ratios, bioreactor volume, HEK cell density and harvest time. These insights enable the identification of optimal conditions for scale up and critical quality attributes (CQAs).
Utilize simulations to analyze the effects of altering triple transfection molar ratios, bioreactor volume, HEK cell density and harvest time. These insights enable the identification of optimal conditions for scale up and critical quality attributes (CQAs).
Utilize simulations to analyze the effects of altering triple transfection molar ratios, bioreactor volume, HEK cell density and harvest time. These insights enable the identification of optimal conditions for scale up and critical quality attributes (CQAs).
Required Input Data
Plasmid Sequences and Annotations
Plasmid Concentration/Volume Details
Bioreactor Details
Advanced Bioreactor Details (optional)
Reduce costs by avoiding expensive bioreactor runs of suboptimal constructs
Save time by reducing the number of test manufacturing cycles for a therapy candidate
Enhance safety by identifying propensities for empty capsids and other impurities
Compare an array of constructs with different design characteristics to find the most promising versions
Fully understand constructs with automated AAV characterization reporting
Enhance constructs to generate de novo designs with the best predicted yield, efficacy and safety characteristics
