Resilient Pharmaceutical Supply Chain Design – Case Studies in Drug Discovery
PhDs and postgraduate research
Self-funded PhD students only
School of Mathematics and Physics
October and February
Applications accepted all year round
The work on this project could:
- Review the regulations and policies in different locations in the DSC.
- Identify and evaluate the operation and disruption risks in the DSC of the drug discovery.
- Assess the stability of the compounds of different formats and determine the routes for the delivery of the right compounds of right format to the right location for assays. The total cost of the delivery should be minimized.
- Reduce the CO 2 emissions in the whole process of the drug discovery.
- 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.
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 aging 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. Thus, 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. Operational risk 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.
Fees and funding
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).
2020/2021 fees (applicable for October 2020 and February 2021 start)
Home/EU/CI full-time students: £4,407 p/a*
Home/EU/CI part-time students: £2,204 p/a*
International full-time students: £16,400 p/a*
International part-time students: £8,200 p/a*
*All 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 (email@example.com) to discuss your interest before you apply, quoting the project code.
When you are ready to apply, you can use our online application form. 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.
If you want to be considered for this PhD opportunity you must quote project code SMAP5360220 when applying.