Spotlighting rising researcher Pradnya Bapat: bridging formulation design and biorelevant performance: innovations in polymer-based dispersions

The Virtual Product-Process Design Laboratory for Design and Analysis of Formulations

Property cluster based visual technique for synthesis and design of formulations

Chapter 17 - Oral Formulations for Preclinical Studies: Principle, Design, and Development Considerations

Computer aided formulation design based on molecular dynamics simulation: Detergents with fragrance

Role of rheology in formulation and process design of hot melt extruded amorphous solid dispersions

Peptide Drug Delivery Strategies for the Treatment of Diabetes

The pharmaceutical product development process is a challenging process involving two significant steps, namely formulation development and product manufacturing. A formulation comprises of an API/drug (pharmacological active compound) and a group of inactive substances known as excipients. The process of selecting excipients and their proportion in an intended physical form of the drug for its administration (dosage form or pharmaceutical product) is known as the formulation. The selection of excipient(s) is a complex process that depends upon various factors associated with the drug, drug-excipient interaction, and the impact of the excipient on the product efficacy, i.e., its intended attributes like product stability, drug release, bioavailability, and many more. Thus, it involves extensive experimentation and hence is challenging in terms of carrying out these requisite trial runs. The application of artificial intelligence may help in reducing the time required to carry out trials and wastage of resources via providing limited and promising formulation designs based upon the evaluation and correlation of existing experimental data through various networking models. In this manuscript, we have represented the outcome of an AI-based pharmaceutical formulation design model which supports the active involvement of AI into fully automated computer-assisted pharmaceutical product development solution, leading to optimization of resource and overcoming the financial constraints via avoiding excessive wastages expected during product design trials.

SummaryWe study the simultaneous analysis and design (SAND) formulation of the ‘classical’ topology optimization problem subject to linear constraints on material density variables. Based on a dual method in theory, and a primal‐dual method in practice, we propose a separable and strictly convex quadratic Lagrange–Newton subproblem for use in sequential approximate optimization of the SAND‐formulated classical topology design problem.The SAND problem is characterized by a large number of nonlinear equality constraints (the equations of equilibrium) that are linearized in the approximate convex subproblems. The availability of cheap second‐order information is exploited in a Lagrange–Newton sequential quadratic programming‐like framework. In the spirit of efficient structural optimization methods, the quadratic terms are restricted to the diagonal of the Hessian matrix; the subproblems have minimal storage requirements, are easy to solve, and positive definiteness of the diagonal Hessian matrix is trivially enforced.Theoretical considerations reveal that the dual statement of the proposed subproblem for SAND minimum compliance design agrees with the ever‐popular optimality criterion method – which is a nested analysis and design formulation. This relates, in turn, to the known equivalence between rudimentary dual sequential approximate optimization algorithms based on reciprocal (and exponential) intervening variables and the optimality criterion method. Copyright © 2016 John Wiley & Sons, Ltd.

Choosing a solvent and an antisolvent for a new crystallization process is challenging due to the sheer number of possible solvent mixtures and the impact of solvent composition and crystallization temperature on process performance. To facilitate this choice, we present a general computer aided mixture/blend design (CAMbD) formulation for the design of optimal solvent mixtures for the crystallization of pharmaceutical products. The proposed methodology enables the simultaneous identification of the optimal process temperature, solvent, antisolvent, and composition of solvent mixture. The SAFT-γ Mie group-contribution approach is used in the design of crystallization solvents; based on an equilibrium model, both the crystal yield and solvent consumption are considered. The design formulation is implemented in gPROMS and applied to the crystallization of lovastatin and ibuprofen, where a hybrid approach combining cooling and antisolvent crystallization is compared to each method alone. For lovastatin, the use of a hybrid approach leads to an increase in crystal yield compared to antisolvent crystallization or cooling crystallization. Furthermore, it is seen that using less volatile but powerful crystallization solvents at lower temperatures can lead to better performance. When considering ibuprofen, the hybrid and antisolvent crystallization techniques provide a similar performance, but the use of solvent mixtures throughout the crystallization is critical in maximizing crystal yields and minimizing solvent consumption. We show that our more general approach to rational design of solvent blends brings significant benefits for the design of crystallization processes in pharmaceutical and chemical manufacturing.

Computer-aided Design of Solvent Blends for the Cooling and Anti-solvent Crystallisation of Ibuprofen

Computational modeling for formulation design

Formulation design and mechanism study of hydrogel based on computational pharmaceutics theories

Predictive formulation design and accelerated formulation design can lead to the discovery of useful formulations to support drug clinical studies and successful drug approval. Predictive formulation design can also lead to discovery of a path for commercialization, especially for poorly soluble drugs, when the target product profile is well defined and a "learning before doing" approach is implemented. One of the key components of predictive/accelerated formulation design is to understand and leverage the material properties of drug substance including solubility, BCS classification, polymorphs, salt formation, amorphous form, amorphous complex, and stability. In addition, utilizing synchrotron-based PDF (pair distribution function) analysis can provide important structural information for the formulation. This knowledge allows control of physical and chemical stability of the designed product. Finally, formulation design should link to process development following Quality by Design principles, and solid-state chemistry should play a critical role in many of the steps required to achieve Quality by Design, which can lead to successful product development.

Pull-out resistance of glued in rod connection in timber: Reliability analyses using an experimental database

The development of drug delivery systems always goes hand-in-hand with the advancement of material science. The novel synthetic or natural functional materials provide opportunities to design optimal drug delivery systems. Emerging trends in the design and development of drug products indicate ever greater need for characterization of excipients and in-depth understanding of their roles in drug delivery applications. This book presents an integrated approach to the characterization and application of excipients. This chapter provides an overview of excipient applications in formulation design and drug delivery with focus on stability implications of drug-excipient interactions, impact of excipients on drug release and bioavailability, the factors that weigh into excipient selection and formulation design, including excipient functionality.