Personalising dosing of biologic therapies in inflammatory arthritis to maximise cost-benefit

Student thesis: Phd


Background: The disease course of rheumatoid arthritis (RA) varies widely between patients, and various therapeutic options are available. However, none are universally effective, all have a risk of side-effects and currently, all are prescribed on a "trial and error" basis, based on escalating cost and not precision medicine targeted to patient endotype. Previous work has shown that circulating drug concentration levels of tumour necrosis factor inhibitors (TNFi, a class of drug used to treat RA and other autoimmune conditions) are associated with response to treatment. This thesis hypothesised that biological factors, such as protein expression, contribute to variability in circulating drug levels and treatment response to biologic agents in patients with RA. Methods: A population pharmacokinetic (popPK) study was carried out in patients with RA starting either Amgevita or Benepali, which are biosimilar agents for the TNFi agents adalimumab and etanercept, respectively. Model parameter estimates from the popPK study were used to simulate altered dosing intervals of these drugs. Proteomics data was obtained on all patients in the popPK study, as well as an additional cohort of patients with RA starting etanercept, using Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS). Protein expression was regressed against RA clinical outcome measures to determine any associations between protein expression and treatment response. Protein expression was also analysed alongside paired genotype data to determine whether any protein quantitative trait loci (pQTLs) existed. Significant pQTLs were then used to construct a polygenic risk score (PRS) for treatment non-response. Results: 16 patients were recruited to the popPK study; PK parameters were successfully estimated and used to simulate the effect of altered dosing intervals. SWATH-MS was used to generate proteomics data in serum samples from 180 selected patients commencing etanercept recruited to the Biologics in RA Genetics and Genomics Study Syndicate, a prospective multi-centre UK-based observational cohort. Proteomics analysis identified 52 proteins associated with RA clinical outcome measures. A pQTL analysis was carried out using 147 patients from the etanercept sub-cohort. 104 pQTLs were identified, 14 of which overlapped with significant proteins from the regression analysis. A PRS was generated using significant pQTLs, but was not found to be statistically significantly predictive of poor treatment response. Conclusions: The popPK study has provided proof-of-concept for future personalised dosing trials in patients with RA starting TNFi. This thesis has identified several proteins associated with RA clinical outcome measures that also have a genetic basis. Findings require external validation with replication studies in an independent cohort, but once confirmed, this could pave the way for future biomarker and/or drug target development.
Date of Award1 Aug 2022
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorAnne Barton (Supervisor), Darren Plant (Supervisor), Kayode Ogungbenro (Supervisor) & James Bluett (Supervisor)


  • treatment response
  • etanercept
  • Amgevita
  • adalimumab
  • biosimilars
  • bioinformatics
  • biomarkers
  • Benepali
  • personalised dosing
  • population pharmacokinetics
  • bDMARDs
  • biologics
  • inflammatory arthritis
  • rheumatoid arthritis
  • proteomics

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