Transforming synthetic drug manufacturing: novel processes, methods and tools
Lead Research Organisation:
Imperial College London
Department Name: Chemical Engineering
Abstract
The pharmaceutical industry, a key player in UK manufacturing, faces huge challenges in turning promising new molecules into affordable medicines. While synthetic drugs make up the largest part of pharmaceutical companies' drug portfolios, with peptides representing an increasingly important class of drugs, the road from the discovery of a drug molecule to a commercial product that benefits patients remains frustratingly long and arduous, with the total cost of development reaching $2.6bn and 10 years per new chemical entity (NCE) . Manufacturing and formulation can be a rate-limiting and costly step due to the difficulty in achieving the required molecular precision or yield in synthesis, in ensuring high yields during purification, and in producing final products with the stability and efficacy that maximise patient benefit. Furthermore, the pharmaceutical industry is facing a drive to improve the economic and environmental performance of its manufacturing processes, which currently suffer from extremely low material efficiency, with factors of 0.01 to 0.1 not unusual , the production of a large amount of waste and a slow adoption of quality by design (QbD) concepts, especially for more complex products.
This Prosperity Partnership builds on an existing collaboration between leading industrial and academic investigators to address critical issues in our ability to manufacture synthetic drugs in a cost and time effective way. Together, we have identified scientific hurdles that prevent the successful manufacture and delivery to patients of key medicines and we have devised an ambitious research programme to overcome them. A unique and exciting feature of our approach is to draw on expertise and advances in the manufacture of small molecules to enable radical progress in the synthetic manufacture of much larger peptide drugs, considering the entire chain from drug substance to drug product. Our programme will thus deliver fundamental understanding, models, technologies and design methodologies in order to accelerate the synthesis, isolation, purification and formulation of synthetic drugs of varying sizes, from small molecules to peptides, and to push the boundary of feasibility in relation to peptide drugs. Beyond its scientific achievements, the Prosperity Partnership will positioning the UK at the leading edge of expertise and innovation in the manufacturing of high-value synthetic drugs, contributing to the growth of a value-creating innovation ecosystem.
Eli Lilly and the two academic partners have co-created a comprehensive research programme with the ambition to reduce radically the cost, time and risk inherent in the manufacturing of synthetic drugs, bringing health and economic benefits to the UK.
Our research vision is thus to deliver novel systems-based engineering design methods for the rapid development of manufacturing processes for advanced synthetic drugs and drug products, strongly rooted in scientific understanding and building on state-of-the-art manufacturing technologies, explainable AI ,modelling and experimental approaches.
Our programme has been designed around 5 interacting work packages
1. Novel synthesis methods for drug substances (active ingredients), including complex peptides which are a very promising emerging therapy
2. Advanced techniques for drug substance crystallisation based on fundamental thermodynamic modelling
3. Advanced techniques for drug substance purification, including the emerging area of peptide chromatography
4. Advanced manufacturing and stability analysis of drug products. Drug substances must be formulated as drug products which must be proven to be stable over their shelf life. Here will explore the interactions between design, manufacturing and stability.
5. Cross-cutting systems engineering methods for model-based design and operational optimisation
This Prosperity Partnership builds on an existing collaboration between leading industrial and academic investigators to address critical issues in our ability to manufacture synthetic drugs in a cost and time effective way. Together, we have identified scientific hurdles that prevent the successful manufacture and delivery to patients of key medicines and we have devised an ambitious research programme to overcome them. A unique and exciting feature of our approach is to draw on expertise and advances in the manufacture of small molecules to enable radical progress in the synthetic manufacture of much larger peptide drugs, considering the entire chain from drug substance to drug product. Our programme will thus deliver fundamental understanding, models, technologies and design methodologies in order to accelerate the synthesis, isolation, purification and formulation of synthetic drugs of varying sizes, from small molecules to peptides, and to push the boundary of feasibility in relation to peptide drugs. Beyond its scientific achievements, the Prosperity Partnership will positioning the UK at the leading edge of expertise and innovation in the manufacturing of high-value synthetic drugs, contributing to the growth of a value-creating innovation ecosystem.
Eli Lilly and the two academic partners have co-created a comprehensive research programme with the ambition to reduce radically the cost, time and risk inherent in the manufacturing of synthetic drugs, bringing health and economic benefits to the UK.
Our research vision is thus to deliver novel systems-based engineering design methods for the rapid development of manufacturing processes for advanced synthetic drugs and drug products, strongly rooted in scientific understanding and building on state-of-the-art manufacturing technologies, explainable AI ,modelling and experimental approaches.
Our programme has been designed around 5 interacting work packages
1. Novel synthesis methods for drug substances (active ingredients), including complex peptides which are a very promising emerging therapy
2. Advanced techniques for drug substance crystallisation based on fundamental thermodynamic modelling
3. Advanced techniques for drug substance purification, including the emerging area of peptide chromatography
4. Advanced manufacturing and stability analysis of drug products. Drug substances must be formulated as drug products which must be proven to be stable over their shelf life. Here will explore the interactions between design, manufacturing and stability.
5. Cross-cutting systems engineering methods for model-based design and operational optimisation
Planned Impact
The outputs of this prosperity partnership between Eil Lilly, Imperial, and UCL will have the potential to impact the pharmaceutical industry directly, as well as other industries and society. For example, the WP1 outputs, which will reduce cost and time-to market of drug substance synthesis, can produce not only economic but also environmental and broader societal benefits.
It will deliver novel methodologies for (i) drug substance synthesis (ii) drug substance purification and isolation (iii) drug product formulation and stability, and (iv) underpinning process systems engineering methods embodied in a range of tools. These will be demonstrated on a range of industrial case studies and demonstration projects supported by our partner.
The invention and development of such methods and their adoption by industry has the potential to lead to a step change in the manufacturing and quality control of advanced therapeutics. The new synthesis techniques for example will for the first time open up opportunities for larger peptide based therapies. The existing underpinning systems and modelling techniques have been shown in to have a 10-fold return in investment in the sector (Am Ende et al., AIChE An, 2010); We expect the next generation of tools to have at least the same impact.
The beneficiaries include:
Pharma industry and related academia: The recent UK Life Sciences Sector Deal states "The life sciences industry is one of the most important pillars of the UK economy, contributing over £70 billion a year and 240,000 jobs across the country" and highlights "advanced therapies" as one of the key platforms. We have aligned our programme with exactly this opportunity. The integrated work packages have the overarching objective of bringing cost-effective new therapies to market as quickly as possible.
Academics and software companies working on model-based systems engineering: The beneficiaries will be those working in the area of design of experiments, quality by design, advanced thermodynamics, optimization and process analytics and control.
Wider industrial partners: The methods are generic and can be applied to a broad range of problems. The tools will have impact in the wider manufacturing sector ranging from consumer goods to agrochemicals and oil and gas; CPSE's industrial consortium includes representatives from these sectors.
Society: The improvements that will arise from our advancements should support faster development and manufacturing of advanced therapies. Our platform will reduce the time and cost of development for new drug production processes.
We shall ensure effective impact outcomes through:
Training: The researchers will gain career-relevant skills and will benefit from the cross-disciplinary interactions within the team as well as with the industrial partner, including secondment opportunities. Senior representatives from Eli Lilly will act as co-supervisors and provide advice, materials, data and analytical resources. They will also provide input on the quality, timeliness and relevance of the methods developed.
Software dissemination: A range of software tools will be developed in the course of the research and made available to the academic community to download and apply to their models.
Manufacturing technologies: we anticipate new manufacturing technologies (e.g. Nanostar sieving for liquid phase synthesis).
Project website and workshops: The core outputs developed herein will be advertised through a website dedicated to the project. We shall also hold dissemination workshops for end users and regulator representatives near the project end
It will deliver novel methodologies for (i) drug substance synthesis (ii) drug substance purification and isolation (iii) drug product formulation and stability, and (iv) underpinning process systems engineering methods embodied in a range of tools. These will be demonstrated on a range of industrial case studies and demonstration projects supported by our partner.
The invention and development of such methods and their adoption by industry has the potential to lead to a step change in the manufacturing and quality control of advanced therapeutics. The new synthesis techniques for example will for the first time open up opportunities for larger peptide based therapies. The existing underpinning systems and modelling techniques have been shown in to have a 10-fold return in investment in the sector (Am Ende et al., AIChE An, 2010); We expect the next generation of tools to have at least the same impact.
The beneficiaries include:
Pharma industry and related academia: The recent UK Life Sciences Sector Deal states "The life sciences industry is one of the most important pillars of the UK economy, contributing over £70 billion a year and 240,000 jobs across the country" and highlights "advanced therapies" as one of the key platforms. We have aligned our programme with exactly this opportunity. The integrated work packages have the overarching objective of bringing cost-effective new therapies to market as quickly as possible.
Academics and software companies working on model-based systems engineering: The beneficiaries will be those working in the area of design of experiments, quality by design, advanced thermodynamics, optimization and process analytics and control.
Wider industrial partners: The methods are generic and can be applied to a broad range of problems. The tools will have impact in the wider manufacturing sector ranging from consumer goods to agrochemicals and oil and gas; CPSE's industrial consortium includes representatives from these sectors.
Society: The improvements that will arise from our advancements should support faster development and manufacturing of advanced therapies. Our platform will reduce the time and cost of development for new drug production processes.
We shall ensure effective impact outcomes through:
Training: The researchers will gain career-relevant skills and will benefit from the cross-disciplinary interactions within the team as well as with the industrial partner, including secondment opportunities. Senior representatives from Eli Lilly will act as co-supervisors and provide advice, materials, data and analytical resources. They will also provide input on the quality, timeliness and relevance of the methods developed.
Software dissemination: A range of software tools will be developed in the course of the research and made available to the academic community to download and apply to their models.
Manufacturing technologies: we anticipate new manufacturing technologies (e.g. Nanostar sieving for liquid phase synthesis).
Project website and workshops: The core outputs developed herein will be advertised through a website dedicated to the project. We shall also hold dissemination workshops for end users and regulator representatives near the project end
Publications
Adjiman C
(2021)
Process Systems Engineering Perspective on the Design of Materials and Molecules
in Industrial & Engineering Chemistry Research
Al Musaimi O
(2022)
Strategies for Improving Peptide Stability and Delivery
in Pharmaceuticals
Al Musaimi O
(2023)
Successful synthesis of a glial-specific blood-brain barrier shuttle peptide following a fragment condensation approach on a solid-phase resin.
in Journal of peptide science : an official publication of the European Peptide Society
Al Musaimi O
(2023)
Prediction of peptides retention behavior in reversed-phase liquid chromatography based on their hydrophobicity.
in Journal of separation science
Al Musaimi O
(2022)
Greener Cleavage of Protected Peptide Fragments from Sieber Amide Resin.
in ChemistryOpen
Al Musaimi O
(2023)
Factors Influencing the Prediction Accuracy of Model Peptides in Reversed-Phase Liquid Chromatography
in Separations
Bascone D
(2020)
Hybrid Mechanistic-Empirical Approach to the Modeling of Twin Screw Feeders for Continuous Tablet Manufacturing
in Industrial & Engineering Chemistry Research
Beran GJO
(2022)
How many more polymorphs of ROY remain undiscovered.
in Chemical science
Bernet T
(2023)
Modeling the Thermodynamic Properties of Saturated Lactones in Nonideal Mixtures with the SAFT-? Mie Approach
in Journal of Chemical & Engineering Data
Besenhard M
(2021)
Recent advances in modelling and control of liquid chromatography
in Current Opinion in Chemical Engineering
Bowskill D
(2021)
Crystal Structure Prediction Methods for Organic Molecules: State of the Art
in Annual Review of Chemical and Biomolecular Engineering
Cruz-Cabeza A
(2020)
Can solvated intermediates inform us about nucleation pathways? The case of ß- p ABA
in CrystEngComm
Gui L
(2023)
Integrating model-based design of experiments and computer-aided solvent design
in Computers & Chemical Engineering
Gui L
(2023)
Uncovering the Most Kinetically Influential Reaction Pathway Driving the Generation of HCN from Oxyma/DIC Adduct: A Theoretical Study.
in Industrial & engineering chemistry research
Guo M
(2023)
The Effect of Chain Length and Conformation on the Nucleation of Glycine Homopeptides during the Crystallization Process.
in Crystal growth & design
Guo M
(2021)
Triglycine (GGG) Adopts a Polyproline II (pPII) Conformation in Its Hydrated Crystal Form: Revealing the Role of Water in Peptide Crystallization.
in The journal of physical chemistry letters
Guo M
(2022)
The effect of chain length and side chains on the solubility of peptides in water from 278.15 K to 313.15 K: A case study in glycine homopeptides and dipeptides
in Journal of Molecular Liquids
Haslam A
(2020)
Expanding the Applications of the SAFT-? Mie Group-Contribution Equation of State: Prediction of Thermodynamic Properties and Phase Behavior of Mixtures
in Journal of Chemical & Engineering Data
Jaradat D
(2022)
Advances in solid-phase peptide synthesis in aqueous media (ASPPS)
in Green Chemistry
Jonuzaj S
(2022)
14th International Symposium on Process Systems Engineering
Kappatou CD
(2023)
An Optimization Approach Coupling Preprocessing with Model Regression for Enhanced Chemometrics.
in Industrial & engineering chemistry research
Katsoulas K
(2023)
On the apparent dispersion coefficient of the equilibrium dispersion model: An asymptotic analysis
in Journal of Chromatography A
Kusumo K
(2022)
Risk mitigation in model-based experiment design: A continuous-effort approach to optimal campaigns
in Computers & Chemical Engineering
Kusumo K
(2021)
31st European Symposium on Computer Aided Process Engineering
| Description | The key findings impact every aspect of pharmaceutical process development: In drug substance synthesis, we have developed novel strategies to controllably manipulate membrane performance (permeability and selectivity) by various means including dope composition, drying conditions and surface grafting; this can increase yield/reduce waste and cost in manufacturing. We have achieved mechanistic understanding of hydrogen cyanide formation in amino acid activation and a systematic in silico method to identify reaction solvents that suppress this unsafe byproduct, to increase the safety of peptide synthesis. We have developed computer-aided design approaches to identify synthesis routes with better yield and reduced environmental impact, through solvent design or build design (linear vs fragment-based). We have also advanced the design and synthesis of novel hubs for peptide synthesis, and developed computational methods to predict the solubility of hubs, accelerating the design process. In drug substance purification isolation via crystallisation, good progress has been made on experimental and computational methods to accelerate crystallisation design. We have developed new experimental protocols to measure accurately the solubility of short chain peptides; generated new data for dipeptides and homopeptides with insights on effects of chain length, solvent, sequence; identified new crystals of GG353 and some of its fragments identified for the first time, although such crystals had been elusive in previous studies; produced the first successful predictions of the solubility of several amino acids and dipeptides in water and alcohols, validated against experimental data from the literature and from the project. We have in addition developed a modelling framework for charged active pharmaceutical ingredients, proposing new theory and validating the application to pH effects on the solubility of pharmaceuticals. We have uncovered a new phase stability criterion for charged systems and used this to propose new algorithms for phase stability analysis in charged and reacting mixtures and parameter estimation for such systems. This has applications much beyond pharmaceuticals. Through algorithmic development, we have achieved reductions of up to 60% in computational time in crystal structure prediction (CSP) by exploiting the mathematical and physical properties of crystals. Finally, we have proposed novel method for screening co-crystallising agents developed and applied them successfully to three pharmaceutical compounds in a blind context. In drug substance isolation and purificatio via high pressure liquid chromatography (HPLC), we have combined experimental and computational methods to propose a new workflow for HPLC design, that can improve product quality and reduce the time and cost of design. This includes creating a digital twin of HPLC for molecules/analytes, using mechanistic and ML models and data to predict retention. This has been validated, in excellent agreement with operational data. We have proposed a superstructure optimisation strategy built to optimise preparative scale operation implement and tested it on initial case studies. We have put forward a strategy to extend work on small moelcules to RP-HPLC for larger molecules (peptides) by integrating peptide molecular properties. Finaly, we have proposed a protocol for advanced experimental characterisation of RPLC stationary phases and column hydrophobicity. In drug product manufacture, stability and delivery, we have developed new mathematical models of powder flow and mixing in continuous screw mixers, implementing rheology models and mixing/segregation models, in excellent agreement with data. We have generated new data from experimental studies of peptide aggregation behaviour using dynamic light scattering and size exclusion chromatography for model peptides. We have successfully tested the effectiveness of new functionalised media for the immobilisation of lipid layers and enzymes onto chromatographic media. Finally, we have developed a set of underpinning systems technologies to support all of the above activities. This includes an improved automated decision-making processes for the generation and maintenance of the chemometric models used in process analytical technology; an efficient approach to identify probabilistic design spaces for complex and highly nonlinear models; a new approach for the design of experiments subject to conflicting objectives, which has so far been tested on simple examples. |
| Exploitation Route | The outcomes are in the form of methods, software and data, that can all be used by others to accelerate process development. All approaches are applicable to small molecules or peptides, but can also be extended to other new modalities such as oligonucleotides, a fast growing type of therapy. In addition, many of the approaches can be used in other contexts, e.g., battery modelling, CO2 capture process design etc. |
| Sectors | Chemicals,Energy,Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology |
| Description | Our findings have been used in industry to improve process development, reducing time needed for new designs, reducing the risks inherent in process development and helping to achieve greater product quality. They have led to changes in workflow and the adoption of new software. |
| First Year Of Impact | 2022 |
| Sector | Chemicals,Energy,Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology |
| Impact Types | Economic |
| Description | ADOPT - Advancing optimisation technologies through international collaboration |
| Amount | £1,344,649 (GBP) |
| Funding ID | EP/W003317/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 02/2022 |
| End | 02/2026 |
| Description | ATLAS - Automated high-throughput platform suite for accelerated molecular systems discovery |
| Amount | £1,281,110 (GBP) |
| Funding ID | EP/V029142/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 06/2021 |
| End | 06/2024 |
| Description | BASF / RAEng Research Chair in Data-Driven Optimisation |
| Amount | £216,000 (GBP) |
| Organisation | Royal Academy of Engineering |
| Sector | Charity/Non Profit |
| Country | United Kingdom |
| Start | 03/2022 |
| End | 02/2027 |
| Description | BioSMART: BIOreactor Spatial Mapping and Actuation in Real Time |
| Amount | £1,011,858 (GBP) |
| Funding ID | EP/W024969/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 08/2022 |
| End | 08/2024 |
| Description | Computer aided solvent design to minimise solvent use in integrated synthesis, purification & isolation for sustainable pharmaceutical manufacturing |
| Amount | £1,351,896 (GBP) |
| Funding ID | EP/W01923X/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 07/2022 |
| End | 07/2025 |
| Description | DEVELOPMENT OF A MACHINE LEARNING-ASSISTED DIGITAL TWIN PLATFORM FOR REAL-TIME OPTIMISATION OF REACTION SYSTEMS UNDER UNCERTAINTY |
| Amount | £714,354 (GBP) |
| Funding ID | EP/X024016/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 09/2023 |
| End | 09/2026 |
| Description | Development and Application of Probabilistic Quantification within Pharmaceutical Manufacturing |
| Amount | £81,400 (GBP) |
| Funding ID | 2767857 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 09/2020 |
| End | 03/2024 |
| Description | Digitalisation and automation of high-value biomanufacturing |
| Amount | £754,778 (GBP) |
| Funding ID | EP/X024156/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 03/2023 |
| End | 03/2026 |
| Description | Nanostar Sieving for Oligonucleotides Manufacture (NanoSieveOligo) |
| Amount | £486,259 (GBP) |
| Funding ID | EP/T00827X/2 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 06/2021 |
| End | 06/2023 |
| Description | Precise deposition of complex particles for structured functional products |
| Amount | £218,888 (GBP) |
| Funding ID | EP/V003070/2 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 02/2022 |
| End | 09/2022 |
| Description | Predicting the thermodynamic stability of multicomponent solids |
| Amount | £40,500 (GBP) |
| Organisation | Syngenta International AG |
| Department | Syngenta Ltd (Bracknell) |
| Sector | Private |
| Country | United Kingdom |
| Start | 10/2021 |
| End | 03/2025 |
| Description | Prosperity Partnership Call 3 Strategic Students-Eli Lilly and Imperial College London |
| Amount | £657,409 (GBP) |
| Funding ID | EP/T518207/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 10/2020 |
| End | 10/2024 |
| Description | SCALING-UP OF A HIGHLY MODULAR ROTATING PACKED BED PLANT WITH AN EFFICIENT SOLVENT FOR CAPTURE COST REDUCTION |
| Amount | € 6,330,101 (EUR) |
| Funding ID | 101075727 |
| Organisation | European Commission H2020 |
| Sector | Public |
| Country | Belgium |
| Start | 11/2022 |
| End | 10/2026 |
| Description | SynHiSel |
| Amount | £7,328,275 (GBP) |
| Funding ID | EP/V047078/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 02/2022 |
| End | 01/2027 |
| Description | System Builders - Device Assembly from Nanoporous Materials Developed from Current Platform Grant (EP/J014974/1) |
| Amount | £524,052 (GBP) |
| Funding ID | EP/R029180/2 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 07/2021 |
| End | 06/2023 |
| Title | AcRoPLS - Accurate Robust PLS (Chryssoula Kappatou) |
| Description | AcRoPLS (Acurate Robust PLS) provides a methodology to create accurate and robust PLS models based on solving a data-driven optimization problem that couples data pre-processing and model regression to a single optimization step. The accuracy objective is evaluated based on the performance of the generated model on predicting the model output on a test set. For the robustness objective, a novel metric based on the method of moments applied for different realizations of a known variability source evaluated again on a test set. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| Impact | The dataset provides a methodology to create accurate and robust PLS models based on solving a data-driven optimization problem that couples data pre-processing and model regression to a single optimization step. |
| URL | https://github.com/ckappatou/AcRoPLS |
| Title | An approach for modelling simultaneous fluid-phase and chemical reaction equilibria in multicomponent systems via Lagrangian duality: The reactive HELD algorithm. |
| Description | This is a data set associated with the paper An approach for modeling simultaneous fluid-phase and chemical reaction equilibria in multicomponent systems via Lagrangian duality: The reactive HELD algorithm. by Felipe A. Perdomo, George Jackson, Amparo Galindo, Claire S. Adjiman. The manuscript is presented as a proceeding of the 33rd European Symposium on Computer-Aided Process Engineering (ESCAPE33), June 18-21, 2023, in Athens, Greece. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| Impact | Conference presentation; used for project in pharmaceutical development and CO2 capture |
| URL | https://zenodo.org/record/7418447 |
| Title | Bootstrap PLS (James Odgers) |
| Description | Files contain methods to produce Design Space (DS) and probabilistic predictions. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | No |
| Impact | The file contains all of the methods used to make probabilistic predictions. |
| URL | https://github.com/jamesacodgers/bootstrapPLS |
| Title | Computer-aided solvent mixture design for the crystallisation and isolation of mefenamic acid |
| Description | The files contain the MINLP formulations presented in this publication. The MINLP problems are implemented and solved in GAMS version 28.2.0, using SBB, a local branch-&-bound MINLP solver. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| Impact | Application to pharmaceutical process development |
| URL | https://zenodo.org/record/3628747 |
| Title | Description of the thermodynamic properties and fluid-phase behaviour of aqueous solutions of linear, branched, and cyclic amines. AIChE J 2021 |
| Description | All computational data for figures presented in the publication. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| Impact | Use in the modelling of complex mixtures of fluids, including in pharmaceutical process development and in CO2 capture. |
| URL | https://zenodo.org/record/4488482 |
| Title | ENTMOOT: A Framework for Optimization over Ensemble Tree Models |
| Description | Gradient boosted trees and other regression tree models perform well in a wide range of real-world, industrial applications. These tree models (i) offer insight into important prediction features, (ii) effectively manage sparse data, and (iii) have excellent prediction capabilities. Despite their advantages, they are generally unpopular for decision-making tasks and black-box optimization, which is due to their difficult-to optimize structure and the lack of a reliable uncertainty measure. ENTMOOT is our new framework for integrating (already trained) tree models into larger optimization problems. The contributions of ENTMOOT include: (i) explicitly introducing a reliable uncertainty measure that is compatible with tree models, (ii) solving the larger optimization problems that incorporate these uncertainty aware tree models, (iii) proving that the solutions are globally optimal, i.e. no better solution exists. In particular, we show how the ENTMOOT approach allows a simple integration of tree models into decision-making and black-box optimization, where it proves as a strong competitor to commonly-used frameworks. |
| Type Of Material | Computer model/algorithm |
| Year Produced | 2022 |
| Provided To Others? | No |
| Impact | The contributions of ENTMOOT include: (i) explicitly introducing a reliable uncertainty measure that is compatible with tree models, (ii) solving the larger optimization problems that incorporate these uncertainty aware tree models, (iii) proving that the solutions are globally optimal, i.e. no better solution exists. In particular, we show how the ENTMOOT approach allows a simple integration of tree models into decision-making and black-box optimization, where it proves as a strong competitor to commonly-used frameworks. |
| URL | https://www.researchgate.net/publication/351340218_ENTMOOT_A_framework_for_optimization_over_ensembl... |
| Title | Model-Based Solvent Selection for the Synthesis and Crystallisation of Pharmaceutical Compounds |
| Description | The pharmaceutical industry needs design tools to identify greener and more resource-efficient process routes. Current model-based solvent selection methodologies often focus on the choice of solvent in a single unit operation, with fixed operating conditions. In particular, the two key stages of synthesis and separation are usually treated independently. This often results in the use of different solvents for each processing task, which typically requires energy-intensive solvent swap operations. In the current paper, we present a novel computer-aided approach based on computer-aided mixture/blend design (CAMbD) that couples property prediction with simple process models and optimisation to simultaneously identify optimal solvents and anti-solvents, compositions and process conditions for integrated synthesis and crystallisation. Solvents are chosen using key performance indicators (KPIs) that quantify mass efficiency and product quality. The proposed methodology is illustrated by identifying promising reaction and crystallisation solvents for the synthesis of mefenamic acid from 2,3-dimethylaniline and 2-chlorobenzoic acid. Furthermore, multi-objective optimisation is deployed to highlight the trade-offs between the solvent or process E-factor and safety indicators, and between the solvent E-factor and crystal yield. The inclusion of mass-based KPIs and safety specifications ensures that only high-performance solvents are chosen. The findings of our approach are expected to guide the rational selection of solvents for greener pharmaceutical manufacturing during early-stage process development. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| Impact | Paper; Use in pharmaceutical process development projects |
| URL | https://zenodo.org/record/5555796 |
| Title | Model-based solvent selection for integrated synthesis, crystallisation and isolation processes |
| Description | We present a systematic process-wide solvent selection tool based on computer-aided mixture/blend design (CAMbD) (Gani, 2004) for the integrated synthesis, crystallisation and isolation of pharmaceutical compounds. The method proposed simultaneously identifies the solvent and/or antisolvent mixture, mixture composition and process temperatures that optimise one or more key performance indicators. Additionally, the method entails comprehensive design specifications for the integrated process, such as the miscibility of the synthesis, crystallisation and wash solvents. The design approach is illustrated by identifying optimal solvent mixtures for the synthesis, crystallisation and isolation of mefenamic acid. Furthermore, a multi-objective CAMbD problem is formulated and shows that a mefenamic acid with purity of 98.8% can be achieved without significant loss of process performance in terms of the solvent E-factor. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| Impact | Use in the development of pharmaceutical processes. New funding secured. |
| URL | https://zenodo.org/record/5585546 |
| Title | OMLT Optimisation and Machine Learning Toolkit |
| Description | The optimization and machine learning toolkit (OMLT) is an open-source software package incorporating neural network and gradient-boosted tree surrogate models, which have been trained using machine learning, into larger optimization problems. OMLT seamlessly integrates with the algebraic modeling language Pyomo. |
| Type Of Material | Computer model/algorithm |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| Impact | Higher-level representations, such as those available in ONNX, Keras, and PyTorch, are very useful for modelling neural networks and gradient-boosted trees. OMLT extends the usefulness of these representations to larger decision-making problems by automating the transformation of these pre-trained models into variables and constraints suitable for optimization solvers. |
| URL | https://github.com/cog-imperial/OMLT |
| Description | Imperial College / CCDC collaboration |
| Organisation | Cambridge Crystallographic Data Centre |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Providing direction, supervision, contributing to the generation of results |
| Collaborator Contribution | Providing direction, supervision, contributing to the generation of results |
| Impact | A methodology to identify intramolecular hydrogen bonding within molecular crystals. |
| Start Year | 2020 |
| Description | PharmaSEL |
| Organisation | Eli Lilly & Company Ltd |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | We are working closely with our partners as part of this Prosperity Partnership in order to transform medicines manufacturing for new modalities (e.g., peptides) as well as small molecules. |
| Collaborator Contribution | Our partner is providing scientific input and sharing insights into the priorities and challenges faced by industry. |
| Impact | A number of papers have been published. This collaboration includes chemical engineering and chemistry. |
| Start Year | 2017 |
| Title | CrystalPredictor 2 |
| Description | Software for global search of crystal structures of organic molecules |
| Type Of Technology | Software |
| Year Produced | 2022 |
| Impact | Use to reduce risk of finding unknown crystal structures; further funding |
| Title | DEUS - DEsign under Uncertainty using Sampling techniques |
| Description | DEsign under Uncertainty using Sampling techniques (DEUS for short) offers Nested Sampling based methods for solving the following problems: - Bayesian parameter estimation - Set-membership estimation - Design space characterization |
| Type Of Technology | Software |
| Year Produced | 2021 |
| Open Source License? | Yes |
| Impact | DEUS forms the basis for several publications on design space characterization in pharmaceutical manufacturing: https://doi.org/10.1021/acs.iecr.9b05006 https://doi.org/10.1016/B978-0-12-823377-1.50327-X https://doi.org/10.1016/j.ifacol.2020.12.555 https://doi.org/10.1016/j.ifacol.2021.08.222 |
| Title | Pydex - Python Design of Experiments |
| Description | An open-source Python package for optimal experiment design, essential to a modeller's toolbelt. Pydex helps design maximally informative experimental campaign for collecting data to calibrate a model mathematical, be it a mechanistic model or a statistical model. It is based on the concept of continuous efforts and relies on convex optimization. |
| Type Of Technology | Software |
| Year Produced | 2021 |
| Open Source License? | Yes |
| Impact | - Continuation of the project in collaboration with Siemens PSE for possible integration within the commercial process modeling and optimization platform gPROMS. |
| Description | Imperial Festival |
| Form Of Engagement Activity | Participation in an open day or visit at my research institution |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | Crystal structure prediction is the field of science which aims at predicting what the crystal structure of a molecule will be in a solid. Crystal structure is very important in ensuring that medicines behave like we want them to and for metal to have desired properties. Tempering the chocolate alters the crystal structure of the chocolate to give it more desirable characteristics. PhD students demonstrated the importance of the "right" crystal structure of molecules, using chocolate as an example. Untampered or badly tempered chocolate and tempered chocolate were be on display for people to see which characteristics makes chocolate attractive and desirable and how tempering can help us in getting the desired crystal structure for the case of chocolates. A computer programme helped to recommend an optimal strategy for tempering the molecular characteristics. |
| Year(s) Of Engagement Activity | 2022 |
| URL | https://www.imperial.ac.uk/news/237506/great-exhibition-road-festival-2022-serves/ |
| Description | Research Team Visit to Eli Lilly & Co |
| Form Of Engagement Activity | Participation in an open day or visit at my research institution |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Industry/Business |
| Results and Impact | A team from UCL visited Eli Lilly in Indianapolis to engage with industry experts involved in pharmaceuticals manufacturing. |
| Year(s) Of Engagement Activity | 2022 |