Resilient Pharmaceutical Supply Chain Design – Case Studies in Drug Discovery
Self-funded PhD students only
School of Mathematics and Physics
February and October
Applications accepted all year round
Applications are invited for a self-funded, 3 year full-time or 6 year part-time PhD project, to commence in October 2019 or February 2020. The PhD will be based in the School of Computing and will be supervised by Dr Xiang Song and Dr Graham Wall
The pharmaceutical industry faces a series of significant challenges, increased Research and Development (R&D) costs, increased regulatory barriers, and reduced earnings from expiring drug patents. Society is equally challenged with an ageing population, demand for more treatments whilst addressing the fundamental challenge of combating climate change.
Today’s drugs are targeted at increasingly more specialised areas with fewer patients taking ever more expensive drugs. In response to increased costs and reduced earnings the pharmaceutical industry has externalising many of the traditional research activities through partnerships and contract research organizations (CRO) This leads to new logistical challenges in the Discovery Supply Chain (DSC) to support Network Research Models (NRM) distributed across geographical and organizational boundaries. The pharmaceutical industry is traditionally highly secretive, making development of operational systems to support NRM very challenging. Access to new treatments is critically dependent on the efficiency of the DSC. That means it is vital to effectively manage these complex operational processes.
Furthermore, profit margins can be improved with optimization generating value for both customers and shareholders. In the drug discovery process, millions of potential compounds are tested, to identify a few candidates which are determined to be safe and effective treatments to a target disease. These candidates are then subject to even more scientific investigation to develop a drug suitable for application to human subjects in clinical trials. In order to optimize the DSC it is vital that these complex processes are fully understood.
This project aims at the resilient design of the DSC. According to the data from Edge Software Consultancy (ESC), containers are used to store compounds and these containers are shipped to different partners and CRO for various types of assays (or tests) to evaluate the efficacy and safety of these potential drugs. Operations risks and disruption risks exist all through the whole supply chain. It is important to build a resilient DSC to choose the right numbers and types of containers to deliver the right quantity of compounds to fulfil the different assays requirements on time whilst minimising the total DSC cost and environmental impact.
The result of the research will help reduce the CO 2 emissions in the whole process of the drug discovery and shorten the total drug discovery time by reducing the transportation time and in turn shorten the time-to-market so as to help companies secure very significant returns in the early life of a successful drug.
Funding Availability: Self-funded PhD students only
PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only).
Home/EU full-time students: £4,327 p/a*
Home/EU part-time students: £2,164 p/a*
International full-time students: £15,900 p/a*
International part-time students: £7,950 p/a*
*Fees are subject to annual increase
By Publication Fees 2019/2020
Members of staff: £1,610 p/a*
External candidates: £4,327 p/a*
*Fees are subject to annual increase
You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in an Civil Engineering or related area. In exceptional cases, we may consider equivalent professional experience and/or Qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.
The ideal candidate should have a Master’s degree in Mathematics, Computer Sciences, Business, or related backgrounds. The knowledge or experiences of C++, Xpress Optimization or CPLEX Optimization software would be an advantage.
How to apply
We’d encourage you to contact Dr. Xiang Song (Xiang.email@example.com) to discuss your interest before you apply, quoting the project code MPHY4420219.
When you're ready to apply, you can use our online application form and select ‘Mathematics and Physics’ as the subject area. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV.
Our How to Apply page offers further guidance on the PhD application process.