Digital Assistant for Pharmaceutical Formulation Development
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Solution Overview
Problem
The development of high-quality products, such as drug products, is often time-consuming and cost-intensive, with a high risk of failure, and may not comply with current quality and regulatory standards due to the reliance on experimental methods and individual expertise in formulating active ingredients and excipients.
Innovation Solution
A computer-implemented method and apparatus that receives user input for dosage form, target product profile, and active ingredient properties, calculates key parameters, selects suitable excipients, suggests manufacturing processes, predicts product properties, and identifies suitable formulations to ensure compliance with defined profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If experimental methods driven by individual expertise are used for product development, then formulation development can be performed with existing knowledge, but the process becomes time-consuming and cost-intensive
Solution Approach 1:
The system performs preliminary calculations of key parameters and predicts product properties before actual formulation development begins. By pre-assessing whether active ingredients can meet target product profiles through computational methods, the system eliminates the need for extensive experimental trials, thereby reducing development time while maintaining quality compliance.
Solution Approach 2:
The system creates a virtual model of the formulation development process that replicates and predicts the outcomes of physical experiments. By using computational algorithms to simulate formulation behavior and predict product properties, the system replaces time-consuming lab experiments with virtual copies, achieving the same quality assessment goals much faster.
2Reliability
If experimental methods driven by individual expertise are used for product development, then formulation development can be performed with existing knowledge, but the process becomes cost-intensive
Solution Approach 1:
The system performs preliminary computational assessments to determine early whether a formulation can meet quality targets. By calculating key parameters and predicting properties before committing to expensive experimental development, the system avoids costly failed experiments and reduces overall development costs while ensuring quality compliance is achieved.
Solution Approach 2:
The system replaces expensive physical experimentation with computational virtual models. By using algorithms to predict formulation outcomes and product properties, the system eliminates the need for repeated lab experiments, material consumption, and associated costs, achieving the same quality validation objectives at fraction of the cost.
3Adaptability or versatility
If experimental methods driven by individual expertise are used for product development, then formulation development can be performed flexibly, but the risk of failure increases
Solution Approach 1:
The system provides immediate computational feedback on whether formulated products are likely to meet target product profiles. By calculating key parameters and predicting properties for each formulation candidate, the system guides formulators toward successful outcomes, reducing failure risk while maintaining the flexibility to explore different formulation options through iterative computational assessment.
Data Source
AI summary
In order to facilitate product development, such as pharmaceutical product development, a computer implemented method and an apparatus are proposed that enable formulators to develop robust drug formulations in a cost- and time-efficient manner. To start the development process, the user selects the preferred dosage form (e.g., granules, pellets, capsules, tablets etc.), defines a target profile (e.g., amount of active ingredient per unit, size of dosage form, mechanical strength of dosage form, desired release behaviour etc.) and enters key characteristics of the active ingredient (e.g., true density, particle size distribution data, bulk and tapped density, angle of repose, compressibility and compactibility profile etc.). The identity (e.g., chemical name or structure) of the active ingredient is not necessarily disclosed. The apparatus processes the provided data and calculates key parameters of the AI (e.g., particle size, powder density, powder flow and tabletability) Similar key parameters are calculated for common pharmaceutical excipients and stored in the apparatus. The apparatus then selects all relevant excipients and suggests a suitable manufacturing process. Combinations of active ingredients and excipients qualify as drug formulation if the predicted properties comply with the defined target profile. The following aspects can be considered: solubility and permeability of the active ingredient, dissolution of the active ingredient, probability to pass the content uniformity criteria, flowability of the powder blend, tabletability of the powder blend, mechanical strength and size of the tablet, compatibility of active ingredients and excipients etc.