Quality by digital design in action: a workflow for crystallisation and isolation

Abstract

This paper exemplifies a Quality by Digital Design (QbDD) workflow for the crystallisation and isolation of active pharmaceutical ingredients (APIs). QbDD uses a digital first approach to improve manufacturability and sustainability whilst assuring product quality within practical constraints. This study uses three exemplar compounds (ibuprofen, lamivudine, and AZD0837), each of which presents a different challenge for crystallisation; these include agglomeration, solid state form, and slow growth rates, respectively. These cases are used to evaluate the benefits of the QbDD approach and identify gaps for future research. Results of this work show that the QbDD workflow reduces the number of physical experiments by 28% and the API material usage by 52–65% when compared to comparable API development processes not using this approach. This approach provides a route to practically implement and exploit the benefits of digital tools and overcome digital skill shortages. By exploiting digital tools for process simulation and optimisation, the workflow improves efficiency, even in complex cases where multiple workflow iterations are required. This workflow, therefore, paves the way for more sustainable and cost-effective API production and it promotes future standardisation of digital design in pharmaceutical development.

Introduction

Pharmaceutical research and development (R&D) encompasses the steps to develop the processes and formulated products for an active pharmaceutical ingredient (API) and produce a medication that is safe and effective and of appropriate quality for patient use. To keep up with the ever-increasing speed of drug development (Conroy, 2023) innovation in product and process development must enable the pharmaceutical industry to deliver medicines to patients as fast, cheaply, and sustainably as possible (Schlander et al., 2021). This must be achieved whilst also exploring a large enough design space to reduce the possibility of in-development failures. Quality by design (QbD) is one of the approaches taken to assure the quality of medicines and enabling continual improvement to reduce costs and environmental impact.

In the 1990 s, QbD was proposed by Joseph M. Juran (Juran, 1992) and adopted by the U.S. Food and Drug Administration (FDA) for pharmaceutical use (Caphart et al., 2006). QbD builds on quality by testing (QbT) and quality by control (QbC) (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use, 2008, Juran, 1992). QbD involves identification of a quality target product profile (QTPP) to allow selection of the critical quality attributes (CQAs). Performing risk assessments enables the identification of critical material attributes (CMAs) and critical process parameters (CPPs) and understand their relationship to each other, allowing development of a suitable control strategy (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use, 2010a). By integrating QbD, manufacturers can develop robust, science-driven processes that consistently produce high-quality pharmaceutical compounds, accounting for the influence of material attributes, process parameters and related risk factors (Azad et al., 2021, Barshikar, 2019, Davis and Schlindwein, 2018, Yu et al., 2019).

With the introduction of Industry 4.0 and 5.0 (Arden et al., 2021; Azizi and Barenji, 2023; European Commission and Directorate-General for Research and Innovation, 2022; Gong et al., 2025, Popov et al., 2022) and the associated innovations in digital technology and automation, digitalisation of chemistry manufacturing and control (CMC) development is both feasible and desirable. Quality by Digital Design (QbDD) builds upon the principles of QbD (Yu et al. 2014), integrating digital technologies, data analytics, and modelling tools into the drug development and manufacturing process ((Mustoe et al., 2025) and references therein). In this work, the authors exemplify a QbDD workflow for CMC process development. This digital-first approach allows for a digitally augmented exploration of the process design knowledge space to enable the production of APIs with the desired attributes while reducing development time and costs. This digitally augmented design space should reduce the risk of product development failure and improve product and process resilience. QbDD enables efficient product quality assurance whilst optimising against sustainability and business targets within practical constraints. For the benefits of QbDD to be realised, fully linked systems-level models are required with integration of cyberphysical systems and data architectures.

Workflows for process development have also been reported in other studies (Agrawal et al., 2023, Brown et al., 2018, Cote et al., 2020, Gioiello et al., 2020), and implementation of mechanistic, data-driven, and hybrid modelling has been demonstrated (Nagy, 2008, Pankajakshan et al., 2025, Sansana et al., 2021). These studies have shown the benefits of using predictive models and implementing cyberphysical systems such as self-driving laboratories to investigate individual processes, subprocesses and process options (Aspuru-Guzik Group, 2024, Hein, 2021, Meng and Liu, 2023, Pickles et al., 2024b).

In this article, we describe the workflow proposed by Mustoe et al. and implement for crystallisation, isolation and drying processes for three pharmaceutical compounds: 1) ibuprofen, which is widely studied in the crystallisation literature (Dwivedi et al., 1992, Jolliffe and Gerogiorgis, 2015, Lukman et al., 2015, Mustoe et al., 2025, Nguyen, 2013, Ostrowska et al., 2015); 2) lamivudine, a comparatively lesser-known compound with a handful of crystallisation studies (Du et al., 2015, Harris et al., 1997, Jozwiakowski et al., 1996, Pickles et al., 2024a) and 3) a recent developmental compound produced by AstraZeneca, AZD0837 (Deshmukh et al., 2020, Yang et al., 2024) for which no prior knowledge for the crystallisation and isolation has been reported. Each of these compounds presents a different challenge for the crystallisation and isolation processes. The aim of this paper is to evaluate and quantify the benefits of a QbDD workflow for crystallisation and isolation of 3 APIs using available digital tools, ensuring product quality whilst optimising against industrially relevant manufacturability and sustainability metrics within practical operating constraints.

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Ian Houson, Humera Siddique, Magdalene W.S. Chong, Murray Robertson, Alice J. Turner, Alison Nordon, Amal Osman, Amparo Galindo, Andrew S. Dunn, Blair Johnson, Brahim Benyahia, Cameron J. Brown, Chantal L. Mustoe, Chris J. Price, Chris D. Reilly, Claire Adjiman, George Jackson, Elke Prasad, Gavin Halbert, Helen Feilden, Jan Sefcik, John McGinty, John Robertson, Kenneth Smith, Mais Al-Attili, Mark McGowan, Mariam Siddique, Momina Pathan, Nazer Rajoub, Niki Hamilton, Rachel Feeney, Scott Brown, Stephanie J. Urwin, Thomas Bernet, Thomas Pickles, Wei Li, Alastair J. Florence, Quality by digital design in action: a workflow for crystallisation and isolation, International Journal of Pharmaceutics, 2025, 126343, ISSN 0378-5173, https://doi.org/10.1016/j.ijpharm.2025.126343.

Read also our introduction article on Quality by Design (QbD) here: