Building a Value Added Proposition for Immunogenicity Risk Management

The immunogenicity profile of a biotherapeutic is determined by a multiplicity of factors ranging from product related, patient (host) related, bioanalytical to process or manufacturing related factors. This creates a complex situation that does not allow direct correlation of such risk factors to the observed incidence of immunogenicity. Therefore, a mechanistic understanding of how these risk factors individually or in concert influence the overall incidence and risk of immunogenicity is crucial to design the best benefit/risk profile for a given biotherapeutic in a given indication. In light of the observations that this field of Predictive Immunogenicity has not progressed sufficiently in the past few years, several forums have focused on investigating the impediments and the reasons behind these impediments that contributed to the lack of progress. The Predictive immunogenicity survey and the Open Forum that was conducted under the AAPS NBC banner provided insights into some of the gaps that exist in this regard. One of the key observations was that almost all biotech pharma scientists were familiar with the predictive tools but very few of them were actually using it. This in fact results in very little data coming in the public domain. That cascades into an impression of poor reliability of these tools in predicting clinical relevance of immunogenicity, culminating into very few companies using these tools. This chain must be broken if this field has to progress. At present prediction of immunogenicity is not requested by the regulatory agencies. The lack of such information does not preclude a sponsor from filing and getting approvals of biotherapeutics. Most sponsors are effectively managing the associated immunogenicity risks in clinic for their drugs, with a variety of strategies from medications to monitoring. So then why would a sponsor spend time and money to gather such data? We believe this disconnect is primarily due to the lack of an established “Value Added Proposition” for the employment of predictive immunogenicity exercises in drug development. Investments will flow in this area if the science, the collaborations and the data clearly demonstrate how these predictive immunogenicity studies can directly influence the commercial success or failure of a drug. Therefore we have made an attempt to illustrate through examples the value added proposition by collecting information from literature and various sources in the industry to highlight how predictive immunogenicity efforts can impact the bottom line in due course of time. It is this sort of information that needs to be disseminated to Sr. Management in the Pharma and the Biotech industry who can provide for the funding and influence the course of this discipline leading to more case studies and consolidation of the knowledgebase to further the science of making safe and effective drugs