Conversion of Chord Length Distribution to Particle Size Distribution

This project aims to develop systematic methodology to convert Chord Length Distribution data to Particle Size Distribution for enhancing the prediction of oral absorption of weakly basic drugs. The computational models used in this project will assist in the interpretation of data being collected.

Led by Prof. Vivek Ranade, University of Limerick

Quantification of polymorphs of drug substances

The aim of this project was to perform analytical tests to determine the LOD and LOQ for two polymorphs, namely α and β, in an API which is comprised of a third polymorph - kappa (ĸ). This was achieved through experimental work to determine the solid concentration calibration curves using a number of analytical techniques including XRD, DSC and Raman Spectroscopy.

“The project was successful in determining which analytical method could provide effective quantitation of the two polymorphs, and delivered LOD and LOQ determinations for each technique evaluated.”

Brian Keaveny, Plant Director - Clarochem

CFD study on lab-scale reactor systems

Scale-up Systems initiated a CFD study by SSPC on the hydrodynamics of commonly used lab-scale reactor systems under typical operating conditions. Predictions of mixing using traditional chemical engineering relationships become more difficult as the scale of operation decreases below a few litres. This project completed a number of CFD simulations allowing more detailed process characterization and will enable users of Dynochem software to make more accurate predictions when scaling-up or down.

Led by Prof. Harry Van den Akker, Bernal Institute, University of Limerick.

Detection and quantification of polymorphs of drug substances

This project addressed the limit of detection and quantification of polymorphs of drug substances (DS) in a fixed-dose combination(FDC) drug-product (DP) on Panalytical Empyrean Series 3. The project extended over a period of 4 months with the post-doctoral researcher working between the Alkermes site in Athlone and the University of Limerick.

This project also facilitated knowledge sharing and exchange of best practice approaches between the SSPC researcher and Alkermes staff located in both the Irish and US sites.

Led by Prof. Michael Zaworotko and Dr Rana Sanii, Bernal Institute, University of Limerick.

Mechanistic Modelling of a biologics development process

APC and SSPC conducted a feasibility study to assess the optimal parameters for the Cation Exchange Chromatography step by Chromatography Mechanistic Modelling, using the GoSilico ChromX® software package. The integration of modelling tools into process development of biologics is an important part of the quality by design approach. Mechanistic modelling ensures time and cost‐efficient process optimization by minimizing experimental work. Consequently, the adoption of modelling tools has increased to support process development, process optimization, process characterization and scale-up phases across the pharmaceutical sector.

Led by Dr. Ioscani Jimenez Del Val, University College Dublin

In Vitro In Vivo evaluation of toxicity and efficacy of CBD formulation

This study investigated the impact of CBD formulations with respect to toxicity and bioavailability. The project was a collaboration between DCU and Maynooth University and benefited from the combined expertise and infrastructure to support both in-vitro testing on skin and intestinal cell models and in-vivo testing on larvae to measure the toxicity of compounds and in vivo efficacy.

The use of in-vitro cell line allows the study of various molecular pathways. This study uses the established cell lines, Caco-2 and HT-29, as models of the intestinal epithelia.

In Vivo testing was conducted using Larvae (caterpillars) of Galleria mellonella. The insect immune response is very similar to the innate immune response of mammals and so insects may be used in place of mammals for a wide range of applications and yield results equivalent to those from mammalian testing. Larvae can also be used to study the metabolism of compounds and show similar metabolic process as occur in mammals.

Led by Dr Finbarr O’Sullivan, NICB, Dublin City University and Prof. Kevin Kavanagh, Maynooth University.

Effect of Mesoporous silica on Amorphous Solid Dispersions

The aim of this project is to determine if mesoporous silica can have a protective effect on Amorphous Solid Dispersions (ASD), and so enhance the physical stability and dissolution performance of such formulations.

ASDs are highly useful but physically unstable drug-delivery platforms. Maintaining physical stability over the shelf-life of the product is especially challenging for high drug loaded
ASDs and for ASDs containing APIs that are prone to rapid crystallization. Mesoporous silica may be a useful formulation additive for de-risking the ASD platform. Silica materials can preferentially adsorb water and thus may promote physical stability of the ASD, stabilizing it by avoiding plasticization of the ASD.

Led by Prof. Anne Marie Healy, Trinity College Dublin.

Zinc Ionophoric activity

Zinc has emerged as a potentially important factor in the treatment of COVID-19; it appears that increasing intracellular zinc with ionophores can inhibit the replication of the virus within in vitro models.

The overall goal for the study is to investigate how the putative zinc ionophores counterparts compare. The study focuses on ionophoric mechanisms of transport, cellular growth, cell death mechanisms, zinc uptake and expression of key proteins associated with cellular zinc physiology.

The study will form the basis for more advanced in-depth studies focusing on particular mechanisms and pathways identified during these initial experiments and to establish a set of ‘design rules’ for effective Zn2+ ionophoric activity.

Led by Dr Rob Elmes Maynooth University, Dr Finbarr O’Sullivan DCU and Dr Oisin Kavanagh, University of Limerick

Centrifuge process modelling

This project will develop a predictive model of the Centrifugation process using machine learning algorithms to process data in order to study the effects of centrifugation and drying on the particle size of the API product. The process will be modelled using a combination of auto-encoder and neuro-fuzzy or deep learning approaches.

Led by Prof. Harry Van Den Akker and Dr. Javad Zeinali, Bernal Institute, University of Limerick

Alternate Catalyst Project

This project has a high commercial potential for Lilly. The goal of the project is to determine if an alternate catalyst and/or ligand will lead to an increase in the yield of an isolated product.

Led by Prof. Pat Guiry, University College Dublin

Meso- and Micro-Mixing in a Static Mixer

This international project team brings together expertise from Merck US, MSD Ballydine and SSPC/UL.

The project uses Lattice Boltzmann techniques to study the effect of mixing on yield and selectivity. The goal of this project is to computationally simulate meso-mixing and micro-mixing in a static mixer reactor – where multiple related reactions take place with different reaction rates.

Led by Prof. Harry Van Den Akker, Bernal Institute, University of Limerick

Continuous Anti-solvent Cavi-crystallization

This project aims to develop a PhD candidate Continuous Anti-solvent Cavi-crystallisation in partnership with Pfizer and two UL based SFI research Centres namely: SSPC and CONFIRM.

The goal of the research is to develop a systematic methodology for design, optimisation and scale-up of continuous crystallisers working in combination with hydrodynamic cavitation for realising better control on particle size distribution. The developed insights and computational models will be useful to critically evaluate applications to industrially relevant crystallisations.

Led by Prof. Vivek Ranade and Prof. Gavin Walker, University of Limerick.

“We are delighted to be partnering with SSPC & Confirm on a Continuous Anti-solvent Cavi-crystallisation PhD project. It will have significant benefits for Pfizer, having applications in future manufacturing, enabling research and upskilling in new areas and engagement between our two centres.”

Dr. Lorna Moynihan, Pfizer

Non-Invasive characterisation of Lyophilised Biopharmaceuticals

This 12 month post-doctoral project addresses commercial challenges for Fill Finish Manufacturing of Lyophilised Biopharmaceuticals by the development of an in situ analytical method compliant with the orange guide requirements for ID.

Many products have a frozen intermediate step for DS prior to Fill Finish Manufacturing, often DS is shipped in multiple containers from the DS site to the DP site and as per the orange guide for parenteral biopharmaceuticals, each container is required to be identified. This usually requires liquid state sampling and testing by HPLC or Dot Blot for example. Opportunities to ID the frozen bulk provides a significant improvement to the manufacturing process.

Led by Dr Sarah Hudson, University of Limerick.

A CFD Approach to Aerated Fed-Batch Cell Culture Bioreactors

The goal of this project is to develop a PhD student over a four-year period in the area of computational fluid dynamic modelling. The student will work closely with the Regeneron Process Sciences team and will divide her time between UL and Regeneron thus benefitting from the experience of working as part of an industry-based team. The objective of this study is to investigate Chinese Hamster Ovary (CHO) cell growth and how productivity is affected by aeration, stirring or mixing. The project aims to computationally model the kinetics of cell cultures at varying process scales and will study the effect of different spargers and various flow rates on oxygen mass transfer, CO2 production and cell growth in production bioreactors at various scale.

Led by Prof. Harry Van Den Akker and PhD Roya Jamshidian, University of Limerick.

Continuous Anti-solvent Crystallisation in a Crystalliser without Moving Parts

The goal of this project is to develop a systematic methodology for design, optimisation and scale-up of continuous crystallisers without any moving parts. Fluidic oscillator designs will be developed for enhancing their performance as continuous anti-solvent crystallizers for industrially relevant systems. A base line fluidic oscillator will be configured based on prior studies. Appropriate methodology will be developed for in-line analysis of crystalliser performance. Systematic design of experiments will be developed and executed to generate new insights and useful data. Various attributes of produced crystals will be thoroughly characterised. Computational fluid dynamics models with population balance models will be developed to simulate complex solid – liquid flow during crystallisation in fluidic oscillator. Initially the models will be used for simulating shear, mixing, and residence time distribution. The models and the experimental data will be used to obtain key parameters of crystallisation kinetics. The computational models will be used to explore large parameter space. Influence of uncertainty associated with mixing, nucleation and growth kinetics (activation energy, rate coefficients), supersaturation etc. will be evaluated. Machine learning approaches will be developed and optimised. The obtained optimal neural network structures combined with the CFD+PBM models (hybrid models) will be used to obtain quantitative relationship between key design and operating parameters and crystalliser performance. Hybrid models will allow adequate capturing of complex non linear processes influencing performance. These will be used to guide development of optimal configuration as well as number scale-up options without compromising performance. The models and results will be used to develop key insights and specific recommendations for use of fluidic oscillator as continuous crystallisers. The project aims to computationally model the kinetics of cell cultures at varying process scales and will study the effect of different spargers and various flow rates on oxygen mass transfer, CO2 production and cell growth in production bioreactors at various scale.

Led by Prof. Vivek Ranade and PhD Vaishnavi Honavar, University of Limerick

Thermodynamic Solubility of Biologically Active Molecules

The goal of the project is to develop a novel materials science approach to control the thermodynamic solubility of biologically active molecules such as active pharmaceutical ingredients (APIs) and agrichemical ingredients (AIs). A library of novel pharmaceutical materials which incorporate model compounds, APIs and AIs will be synthesised and structurally characterised. The aqueous solubility of the synthesised materials will then be determined by HPLC and thermal analysis will be used to characterise melting points and other phase transitions to investigate how the physiochemical properties of the API or model compounds are affected. In later stages of the project, the mechanisms of API/model compound dissolution will be investigated. The project also aims to develop modelling and measurement approaches to rapidly translate to broad solid-state discovery. The project aims to computationally model the kinetics of cell cultures at varying process scales and will study the effect of different spargers and various flow rates on oxygen mass transfer, CO2 production and cell growth in production bioreactors at various scale.

Led by Prof. Michael Zaworotko, postdoctoral researchers Dr Rana Sanii and PhDs Daniel O’Hearn and Molly Haskins

Design of Highly specific Gene Knockout Agents

This project aims to design highly specific gene knockout agents by using rational design and structure activity relationship (SAR) methodologies. The DNA damaging agents will initially be composed of copper-based artificial metallo-nucleases (AMNs) tethered to triplex forming oligonucleotides (TFOs) via click chemistry. These will be studied in-vitro for DNA binding, triplex stabilization, and oxidative damaging properties. To achieve this we have segregated the body of work into two parts. Part A aims to further develop stand-alone AMNs previously discovered in our group. Insights into the SAR of AMNs developed in part A will then be used to develop “Ready to Click” AMNs in part B that will then be used for gene-targeting through TFO conjugation.

Led by Assoc. Prof. Andrew Kellett. Postdoctoral researchers: Dr Georgia Menounou and PhD Alex Gibney, Dublin City University

Multi-Product Resin Reuse for Biopharmaceutical Manufacturing

This project investigates the multi-product use of a resin in downstream biopharmaceutical production. Currently resins are used repeatedly for batches of the same product with a predetermined column cleaning regime. The proposed project aims to investigate the feasibility of extending resin reuse to a second product. The proposed project will build on the White Paper ‘Multiproduct Resin Reuse for Clinical and Commercial Manufacturing—Methodology and Acceptance Criteria’ published in the PDA Journal of Science and Technology in 2018. This study concentrated on resin and column performance, product carryover and cleaning effectiveness following use with multiple products.

Led by Prof. Kevin Kavanagh and postdoctoral researchers Dr Anatte Margalit, Maynooth University

Isolation and purification of Hexameal AMP and characterisation of its effect on MRSA

This project builds on work completed in a previous project with Hexafly titled “Isolation, identification and characterisation of an antimicrobial peptide from Hexameal”. Hexafly is an Irish Biotech company that have developed the use of insects as a novel food source. Previously a highly active antimicrobial peptide (AMP) was isolated from insect meal (Hexameal). This project characterised the mode of action of the AMP against a variety of drug resistant bacteria. When MRSA was exposed to the AMP there was a disruption of cell wall synthesis and cell death indicating the potential for the AMP to be used to treat drug resistant bacterial infections.

Led by Prof. Kevin Kavanagh and postdoctoral researchers Dr Anatte Margalit, Maynooth University

Development for freeze drying cycle for large volume EntericBio

The overall aim of this project is to determine ways in which to optimize this process for higher volumes. This will be achieved by first defining the CQAs of the initial standard samples and then revisiting the FD process using DoE as an effective statistical tool for mapping the effect of process factors (temperature, heating and cooling rate at each of the loading, freezing, primary drying, secondary drying and unloading) on the CQAs.

Led by Prof. Gavin Walker, University of Limerick

Cookie	Duration	Description
viewed_cookie_policy	1 year	The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data.
_GRECAPTCHA	5 months 27 days	This cookie is set by Google. In addition to certain standard Google cookies, reCAPTCHA sets a necessary cookie (_GRECAPTCHA) when executed for the purpose of providing its risk analysis.

Cookie	Duration	Description
CONSENT	16 years 6 months 8 days 11 hours	No description
yt-remote-connected-devices	never	No description available.
yt-remote-device-id	never	No description available.

Cookie	Duration	Description
IDE	1 year 24 days	Used by Google DoubleClick and stores information about how the user uses the website and any other advertisement before visiting the website. This is used to present users with ads that are relevant to them according to the user profile.
test_cookie	15 minutes	This cookie is set by doubleclick.net. The purpose of the cookie is to determine if the user's browser supports cookies.
VISITOR_INFO1_LIVE	5 months 27 days	This cookie is set by Youtube. Used to track the information of the embedded YouTube videos on a website.
YSC	session	This cookies is set by Youtube and is used to track the views of embedded videos.

Cookie	Duration	Description
_ga	2 years	This cookie is installed by Google Analytics. The cookie is used to calculate visitor, session, campaign data and keep track of site usage for the site's analytics report. The cookies store information anonymously and assign a randomly generated number to identify unique visitors.
_gat_gtag_UA_157554574_1	1 minute	This cookie is set by Google and is used to distinguish users.
_gid	1 day	This cookie is installed by Google Analytics. The cookie is used to store information of how visitors use a website and helps in creating an analytics report of how the website is doing. The data collected including the number visitors, the source where they have come from, and the pages visted in an anonymous form.

Industry Projects

Industry Project:

Industry Project:

Industry Project:

Industry Project:

Industry Project:

Industry Project:

Industry Project:

Industry Project:

Industry Project:

Industry Project:

Industry Project:

Industry Project:

Industry Project:

Industry Project:

Industry Project:

Industry Project:

Industry Project:

Industry Project:

Industry Project:

Industry Project:

SSPC Members Area

Our People

Our Research

Latest Tweets