Rational Design
Experimental design and analysis
Avantium makes extensive use of statistical Design of Experiments (DoE) to rapidly screen experimental parameters and optimize products and processes.
Experimental design is not about minimizing the number of experiments performed, but rather, about doing the right number of experiments to solve a given problem.
In DoE, many experimental parameters are varied simultaneously with the size and significance of the 'effect' for each variable, subsequently being determined by statistical analysis. This approach enables Avantium to use your experimental budget most efficiently.
In catalyst development studies DoE is used to find optimal catalyst compositions and preparation methods. In process optimization studies, it is used to identify conditions that optimize multiple yields and selectivities, subject to process operating constraints.
DoE is also an important component of chemical formulation development projects, where the aim is to discover optimal recipes and additives. In this case, DoE enables the optimization of product performance e.g. rheology, stability or other physicochemical properties.
Optimization studies typically require the development of a 'high fidelity' empirical model and subsequent use of numerical optimization techniques. Such a model is commonly referred to as a Response Surface Model (RSM).
Rational solvent selection
Avantium uses a rational approach to the selection of crystallization screening solvents that take account of the chemistry of the drug compound, solvent physical properties, crystallization mode and experimental protocol, drug solubility in organic solvents, pharmaceutical acceptability and chemometrically derived measures of solvent diversity.
The starting point for a solvent selection is Avantium's solvent virtual library, containing hundreds of potential screening solvents. All solvents in the library are characterized by molecular descriptors and incorporated into a statistical model of solvent diversity.
On the basis of a limited set of solubility measurements and the results of a thermal analysis, state-of-the-art thermo-physical modeling software is used to estimate compound solubility in a library of diverse organic solvents.
These solubilities and solvent physical properties, such as the boiling point, are then considered in the light of the desired crystallization mode and experimental protocol. Solvents deemed unlikely to result in successful crystallization experiments are removed from the solvent candidate list.
Finally, a diverse set of solvents is selected from solvent principal property space.
A similar 'rational' philosophy is used for the selection of counter-ions in salt screens and co-formers in co-crystallization screens.
Rational design
Avantium has embraced the Rational Design method for efficient screening of large libraries of reagents, solvents and catalysts. This cost-effective approach combines the advantages of molecular modeling, statistical design of experiments and multivariate statistics.
The development of QSPRs (Quantitative Structure-Property Relationships) is core to the Rational Design methodology. The underlying assumption in QSPR entails that it is possible to predict the properties or performance of compounds by knowing the molecular structures.
Candidate molecules are first characterized by molecular 'descriptors' that quantify their physicochemical properties. These descriptors are subsequently analyzed using statistical techniques to facilitate the systematic exploration of the available chemical diversity and select an optimal subset of compounds for testing.
Once experimental response data has been obtained (e.g. rate, yield or enantio-selectivity), a QSPR model can be generated to relate differences in the molecular descriptors to differences in the responses. Such a model can subsequently be used to predict the performance of compounds that have not been tested.
This approach allows large libraries of reagents, solvents and catalysts to be virtually screened, while only those molecules that are predicted to perform well need to be synthesized and tested.
Avantium has successfully applied Rational Design and QSPR techniques to:- Enantiomeric excess and rate prediction in asymmetric catalytic reactions
- E-value and rate prediction for biocatalysts
- Formulation additive optimization
- Physical property and solubility prediction
- As well as a variety of other applications.
Avantium would be pleased to discuss the use of the Rational Design method and QSPR in product or process optimization or the possibility of integrating Avantium's modelling expertise with its customers' high-throughput experimentation capabilities.
Kinetic modeling
Avantium has developed proprietary kinetic modeling tools that can transform data from Avantium's Nanoflow fixed-bed catalyst testing systems into detailed mechanistic models. In this way, intrinsic catalyst kinetics can be obtained, multiple reaction mechanisms can be compared and deep insight into catalyst performance can be gained. The information obtained can be fed directly into improved catalyst or process design, process control or feedstock utilization.
Rapid parallel testing in continuous operation allows the simultaneous exploration of catalyst and process variables. The data from batch experiments and continuous flow experiments can be used to develop kinetic models and to describe catalyst performance as a function of process conditions. Kinetic modeling is usually seen as a method to optimize process operation, but, when applied as part of a catalyst development program, kinetic modeling can also provide insight into the reaction mechanism in relation to the catalyst composition. It can also be of use in directing the scope of the program.
At Avantium, a typical kinetic program entails the study of a mechanism by use of different feed compositions (concentrations and components) and space velocities and study of internal and external mass transfer effects.
The ideal circumstances created by Avantium's Nanoflow systems (isothermal conditions, plug flow conditions, limited pressure drop, and ability to test small particles) enable the derivation of intrinsic catalyst kinetics.
Avantium can also utilize parallel testing and kinetic fitting so as to derive efficiently and rapidly the most likely mechanism of catalyst deactivation. This can form the basis for the development of accelerated deactivation test procedures.