This manuscript represents the perspective of the Dissolution Working Group of the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) and of two focus groups of the American Association of Pharmaceutical Scientists (AAPS): Process Analytical Technology (PAT) and In Vitro Release and Dissolution Testing (IVRDT). The intent of this manuscript is to show recent progress in the field of in vitro predictive dissolution modeling and to provide recommended general approaches to developing in vitro predictive dissolution models for both early- and late-stage formulation/process development and batch release. Different modeling approaches should be used at different stages of drug development based on product and process understanding available at those stages. Two industry case studies of current approaches used for modeling tablet dissolution are presented. These include examples of predictive model use for product development within the space explored during formulation and process optimization, as well as of dissolution models as surrogate tests in a regulatory filing. A review of an industry example of developing a dissolution model for real-time release testing (RTRt) and of academic case studies of enabling dissolution RTRt by near-infrared spectroscopy (NIRS) is also provided. These demonstrate multiple approaches for developing data-rich empirical models in the context of science- and risk-based process development to predict in vitro dissolution. Recommendations of modeling best practices are made, focused primarily on immediate-release (IR) oral delivery products for new drug applications. A general roadmap is presented for implementation of dissolution modeling for enhanced product understanding, robust control strategy, batch release testing, and flexibility toward post-approval changes.

In this study, the influence of key process variables (screw speed, throughput and liquid to solid (L/S) ratio) of a continuous twin screw wet granulation (TSWG) was investigated using a central composite face-centered (CCF) experimental design method. Regression models were developed to predict the process responses (motor torque, granule residence time), granule properties (size distribution, volume average diameter, yield, relative width, flowability) and tablet properties (tensile strength). The effects of the three key process variables were analyzed via contour and interaction plots. The experimental results have demonstrated that all the process responses, granule properties and tablet properties are influenced by changing the screw speed, throughput and L/S ratio. The TSWG process was optimized to produce granules with specific volume average diameter of 150 μm and the yield of 95% based on the developed regression models. A design space (DS) was built based on volume average granule diameter between 90 and 200 μm and the granule yield larger than 75% with a failure probability analysis using Monte Carlo simulations. Validation experiments successfully validated the robustness and accuracy of the DS generated using the CCF experimental design in optimizing a continuous TSWG process.

No abstract

No abstract

No abstract

No abstract

No abstract

The use of hot melt extrusion (HME) to produce pharmaceutical solid dispersions utilises pharmaceutical grade polymer(s), potentially high temperatures and mechanical energy to convert a drug substance from a crystalline to an amorphous state. Process analytical technology (PAT), in general, provides numerous manufacturing advantages but those specific to HME will be highlighted. An in-line, transmission mode, Fourier transform near infrared spectroscopy (FT-NIR) and partial least squares (PLS) method was developed for real-time drug loading (% wt/wt) predictions during hot melt extrusion of a Merck product. The NIR/PLS method has been successfully used for process fault detection and real-time quality assurance via switch gate automation control; however, the focus herein will be the use of the method for process understanding with three specific examples provided.