EMBeDs: Ensuring Manufacture of next generation Biopharmaceuticals by Developability
Lead Research Organisation:
University of Leeds
Department Name: Sch of Molecular & Cellular Biology
Abstract
Monoclonal antibodies (mAb) are medicines that have revolutionised treatment of a broad range of disease states for nearly 3 decades. mAbs are expressed from engineered cells but recently there has been significant effort to enhance their functionality by protein engineering. This next generation of mAb-based therapeutics (NG mAbs) includes bispecifics, mAb-scFv and FC fusions. However, whilst this bring performance gains, the NG mAbs are generally less robust as a result of these modifications. This places a greater emphasis on correctly establishing the stability of potential candidate molecules at an earlier stage in the development cycle, such that "manufacturability" is built into the decision-making process around which candidates to take forward for industrial production.
To de-risk the translation from scientific discovery to production, industry uses developability assays (DAs) to infer the likelihood that a potential candidate can be manufactured robustly at scale to give a safe and stable drug. There is an unmet and timely need to establish appropriate developability assays for this next generation of therapeutics.
Working with industrial partners has highlighted that long term / accelerated stability (LTAS) is a critical quality attribute for candidate medicines that is costly to determine in terms of both time and resource. Ideally, candidates with good LTAS would be identified through the use of DAs, to identify manufacturable product, and at a stage where only small amounts of sample are available and in a short timeframe.
Whilst many assays are available, identifying the optimal subset of DAs that can predict LTAS early on development, would allow resources to be focussed on those sequences likely to be successfully manufactured, reducing time to market and cost-of-goods and increasing sustainability.
Here we propose to use a range of industrially-adopted DAs and manufacturing-focussed assays (developed by the applicants or advanced within this proposal), to perform a detailed, statistically robust investigation of the developability of three next generation formats. This will be carried out alongside LTAS studies, in line with industry practice. Working closely with manufacturing partners, this will support establishing a minimum panel of DAs in assessing manufacturability, was well as advancing scientific understanding that will rationalize molecule design.
In addition, this data set will allow us to:
(1) perform statistical and in silico methods to identify the relationship between each "family" of DAs and stability data for the first time for any format.
(2) convolute liabilities identified by distinct assays into a single developability metric, simplifying the integration of often disparate DAs.
(3) use statistical methods to identify a minimal set of non-degenerate DAs that predict LTAS using a fraction of the time and sample required.
The resulting dataset will be unique in the sector for any format: it will be publicly accessible and contain sequence data, observables from DAs and LTAS data. Any stakeholder will be able to utilise this large dataset for their own analyses, either to generate predictive algorithms or to validate novel manufacturing methods or developability screens, increasing its impact.
This study is timely since we are at the start of the manufacturing era of NG mAbs. To address manufacturability through identification of appropriate assays will provide a rationalised and streamlined pathway towards supply of this important class of medicines. It will facilitate the economic and sustainable translation of candidate therapeutics to blockbuster medicines now and in the future.
To de-risk the translation from scientific discovery to production, industry uses developability assays (DAs) to infer the likelihood that a potential candidate can be manufactured robustly at scale to give a safe and stable drug. There is an unmet and timely need to establish appropriate developability assays for this next generation of therapeutics.
Working with industrial partners has highlighted that long term / accelerated stability (LTAS) is a critical quality attribute for candidate medicines that is costly to determine in terms of both time and resource. Ideally, candidates with good LTAS would be identified through the use of DAs, to identify manufacturable product, and at a stage where only small amounts of sample are available and in a short timeframe.
Whilst many assays are available, identifying the optimal subset of DAs that can predict LTAS early on development, would allow resources to be focussed on those sequences likely to be successfully manufactured, reducing time to market and cost-of-goods and increasing sustainability.
Here we propose to use a range of industrially-adopted DAs and manufacturing-focussed assays (developed by the applicants or advanced within this proposal), to perform a detailed, statistically robust investigation of the developability of three next generation formats. This will be carried out alongside LTAS studies, in line with industry practice. Working closely with manufacturing partners, this will support establishing a minimum panel of DAs in assessing manufacturability, was well as advancing scientific understanding that will rationalize molecule design.
In addition, this data set will allow us to:
(1) perform statistical and in silico methods to identify the relationship between each "family" of DAs and stability data for the first time for any format.
(2) convolute liabilities identified by distinct assays into a single developability metric, simplifying the integration of often disparate DAs.
(3) use statistical methods to identify a minimal set of non-degenerate DAs that predict LTAS using a fraction of the time and sample required.
The resulting dataset will be unique in the sector for any format: it will be publicly accessible and contain sequence data, observables from DAs and LTAS data. Any stakeholder will be able to utilise this large dataset for their own analyses, either to generate predictive algorithms or to validate novel manufacturing methods or developability screens, increasing its impact.
This study is timely since we are at the start of the manufacturing era of NG mAbs. To address manufacturability through identification of appropriate assays will provide a rationalised and streamlined pathway towards supply of this important class of medicines. It will facilitate the economic and sustainable translation of candidate therapeutics to blockbuster medicines now and in the future.