Jamie Iain Campbell

Developing the next generation of monoclonal antibodies for the treatment of rheumatoid arthritis

Rheumatoid arthritis is one of the commonest autoimmune diseases affecting 0.8% of the population. Over the last decade the treatment of this chronic disease has been revolutionized by the use of monoclonal antibodies and fusion proteins, targeting molecules like tumour necrosis factor alpha. Nevertheless, approximately one‐third of subjects fail to respond to these therapies and therefore significant unmet medical need remains. Following a decade of use, clinical, government and regulatory agency expectations have changed for new antibodies therapies entering this highly competitive area. In this review, we discuss the current advances being made in antibody engineering and how they are being considered and used in the development of the next generation of antibodies to meet future expectations of healthcare providers, physicians and patients. Moreover, we discuss how pattern recognition receptors may provide new antibody tractable targets that may break the cycle of autoimmunity in rheumatoid arthritis.

Antibody VH and VL recombination using phage and ribosome display technologies reveals distinct structural routes to affinity improvements with VH-VL interface residues providing important structural diversity.

In vitro selection technologies are an important means of affinity maturing antibodies to generate the optimal therapeutic profile for a particular disease target. Here, we describe the isolation of a parent antibody, KENB061 using phage display and solution phase selections with soluble biotinylated human IL-1R1. KENB061 was affinity matured using phage display and targeted mutagenesis of VH and VL CDR3 using NNS randomization. Affinity matured VHCDR3 and VLCDR3 library blocks were recombined and selected using phage and ribosome display protocol. A direct comparison of the phage and ribosome display antibodies generated was made to determine their functional characteristics.

Pulmonary pharmacodynamics of an anti-GM-CSFRα antibody enables therapeutic dosing that limits exposure in the lung

ABSTRACT Pulmonary alveolar proteinosis is associated with impaired alveolar macrophage differentiation due to genetic defects in the granulocyte macrophage colony-stimulating factor (GM-CSF) axis or autoantibody blockade of GM-CSF. The anti-GM-CSFRα antibody mavrilimumab has shown clinical benefit in patients with rheumatoid arthritis, but with no accompanying pulmonary pathology observed to date. We aimed to model systemic versus pulmonary pharmacodynamics of an anti-GM-CSFRα antibody to understand the pharmacology that contributes to this therapeutic margin. Mice were dosed intraperitoneal with anti-GM-CSFRα antibody, and pharmacodynamics bioassays for GM-CSFRα inhibition performed on blood and bronchoalveolar lavage (BAL) cells to quantify coverage in the circulation and lung, respectively. A single dose of 3 mg/kg of the anti-GM-CSFRα antibody saturated the systemic cellular pool, but dosing up to 10 times higher had no effect on the responsiveness of BAL cells to GM-CSF. Continued administration of this dose of anti-GM-CSFRα antibody for 7 consecutive days also had no inhibitory effect on these cells. Partial inhibition of GM-CSFRα function on cells from the BAL was only observed after dosing for 5 or 7 consecutive days at 30 mg/kg, 10-fold higher than the proposed therapeutic dose. In conclusion, dosing with anti-GM-CSFRα antibody using regimes that saturate circulating cells, and have been shown to be efficacious in inflammatory arthritis models, did not lead to complete blockade of the alveolar macrophages response to GM-CSF. This suggests a significant therapeutic window is possible with GM-CSF axis inhibition.

Haplosaurus computes protein haplotypes for use in precision drug design

Selecting the most appropriate protein sequences is critical for precision drug design. Here we describe Haplosaurus, a bioinformatic tool for computation of protein haplotypes. Haplosaurus computes protein haplotypes from pre-existing chromosomally-phased genomic variation data. Integration into the Ensembl resource provides rapid and detailed protein haplotypes retrieval. Using Haplosaurus, we build a database of unique protein haplotypes from the 1000 Genomes dataset reflecting real-world protein sequence variability and their prevalence. For one in seven genes, their most common protein haplotype differs from the reference sequence and a similar number differs on their most common haplotype between human populations. Three case studies show how knowledge of the range of commonly encountered protein forms predicted in populations leads to insights into therapeutic efficacy. Haplosaurus and its associated database is expected to find broad applications in many disciplines using protein sequences and particularly impactful for therapeutics design.Proteoforms arise as protein isoforms or as protein haplotypes, which are the result of genetic variation. Here, the authors develop Haplosaurus, a database that computes protein haplotypes genome-wide from existing genotype data and analyse protein haplotype variability in the 1000 Genomes dataset.