DIGITAL FORMULATION DESIGN - 2028/9

Module Overview

In this module, you will learn how digital tools and data-driven approaches support modern pharmaceutical formulation development. You will explore methods such as Quality by Design (QbD), Design of Experiments (DoE), PBPK modelling, and data analytics to inform formulation decisions. The module will also introduce artificial intelligence and machine learning applications in pharmaceutics, helping you understand how digital technologies can be used to analyse data and predict key formulation properties.

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2028/9 academic year.

Module provider

Chemistry

Module Leader

ZARMPI Panagiota (Chst Chm Eng)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Module Availability

Semester 1

Prerequisites / Co-requisites

None

Module content

Indicative content includes

Computer-Aided Formulation Design: application of QbD and DoE frameworks. (CHEM042)

Physiologically Based Pharmacokinetic (PBPK) Modelling: application of PBPK models to support formulation decisions.

Data Analytics for Formulation Development: basics of handling data, preparing datasets for analysis, and using visualisation tools to extract insights. PCA, PLS will be explored.

Introduction to Artificial Intelligence (AI) and Machine Learning (ML) in Pharmaceutics: application of ML models to predict critical formulation attributes such as solubility, dissolution rate, and stability.

Assessment pattern

Alternative Assessment

N/A

Assessment Strategy

The assessment strategy is designed to: provide you with opportunities to demonstrate understanding of core principles while developing practical and analytical skills essential for a career in pharmaceutical sciences.

Thus, the summative assessment for this module consists of:

• Portfolio: addresses learning outcomes: 2, 3, 4 and 5

• In class test: addresses learning outcomes: 1, 3 and 4)

Formative assessment is designed to support your learning and development by providing ongoing feedback and opportunities to consolidate knowledge, develop practical skills, and link theory to practice. It includes activities such as in-class quizzes, problem-solving exercises, workshops, and group discussions. In laboratory sessions, formative assessment is provided through guided worksheets, and informal feedback from instructors, enabling you to prepare for experiments, interpret results, and refine practical techniques before submitting summative lab reports. These activities are intended to help you identify areas for improvement, enhance critical thinking, and develop transferable skills such as teamwork, scientific communication, and data analysis, ensuring you are well-prepared for the summative assessments and for progression to more advanced pharmaceutics content in later years.

Feedback will be provided throughout the module during lectures, through interactions in laboratory and workshop sessions, and on individual laboratory reports. This will include general guidance to the whole group as well as specific feedback on work to support learning and development. In addition, one-to-one feedback can be requested by appointment with the module leader or relevant lecturer, providing opportunities for further discussion and clarification of concepts.

Module aims

• Develop advanced understanding of digital and data-driven approaches used in pharmaceutical formulation development
• Critically evaluate the application of QbD, DoE, modelling and data analytics approaches in formulation optimisation
• Develop skills in analysing, interpreting and visualising pharmaceutical datasets
• Evaluate the strengths, limitations and appropriate use of computational models in formulation decision-making
• Introduce the role of AI and machine learning in predicting formulation performance and supporting pharmaceutical innovation

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to: provide you with a solid theoretical foundation in the subject matter while also developing practical, cognitive, and transferable skills essential for future careers.

The learning and teaching methods include:

Lectures: delivered primarily through PowerPoint and led by experienced members of academic staff with subject-specific expertise. The lectures will focus on introducing key concepts, Sessions will be interactive, with regular opportunities for students to ask questions, engage in guided problem-solving and reflective thinking, and receive timely feedback from staff and peers.

Workshops: delivered by experienced academic staff with subject-specific expertise and will adopt a problem-based learning approach centred on relevant, real-world scenarios. Students will be provided with structured exercises that encourage application of core concepts, critical thinking, and professional judgement. Dedicated time will be allocated for students to work through tasks individually and collaboratively, with ongoing opportunities to interact with the lecturer, ask questions, and engage in discussion. Each session will conclude with lecturer-led feedback, including worked solutions and guided reflection, to consolidate learning and support the development of transferable skills relevant to future academic study and professional practice.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: CHEM053

Other information

The school of Chemistry and Chemical Engineering is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience.

In this module, you will develop digital capabilities by learning to use modern software and computational tools for data analysis, modelling, and formulation design, including statistical tools, PBPK modelling platforms, and basic machine learning approaches. These skills are increasingly important in the pharmaceutical industry, supporting employability. Sustainability is embedded by demonstrating how digital modelling and predictive tools can reduce unnecessary experimentation, optimise the use of materials and resources, and support more efficient and responsible pharmaceutical development.