Craig Zwickl

Immunogenicity of biopharmaceuticals in laboratory animals

The potential immunogenicity of new protein therapeutics raises concerns about the possibility of inducing untoward immune reactions in humans. It is generally assumed that all animals will make antibody to human proteins and therefore, there is sentiment among some scientists that this makes the issue of immunogenicity as a safety concern irrelevant. However, recent clinical trials with some proteins have detected the presence of autoantibodies that have resulted in clinical sequelae. These reactions were also observed in preclinical animal studies. In fact, non-human primate and transgenic mouse models can be useful for predicting the relative immunogenicity of human proteins. In addition, the characterization of the immunogenicity of biotechnology molecules provides a practical basis for determining the significance of antibody formation in preclinical safety studies.

Comparison of the Immunogenicity of Recombinant and Pituitary Human Growth Hormone in Rhesus Monkeys

The relative concentrations of antibodies produced in monkeys against three forms of human growth hormone (hGH) were determined using an antigen-specific avidin/biotin ELISA assay. Monkeys were treated in two separate 90-day studies with recombinant methionyl-hGH (met-hGH) and pituitary-derived hGH (pit-hGH) (Study 1) and recombinant natural sequence hGH (Study 2). The lowest dose was equal to the expected therapeutic dose of 0.1 IU/kg. Sixty-nine percent of monkeys treated with pit-hGH and 81% of those treated with met-hGH developed detectable anti-hGH responses. The magnitudes of the responses exhibited wide animal to animal variability, were not markedly related to dose or sex, and were lower than levels obtained in monkeys immunized with hGH in Freunds adjuvant. In contrast, the incidence of antibody responses in monkeys treated with natural sequence hGH was lower (23% in one experiment and 5% in a replicate experiment) and took longer to develop. Antibody concentrations were lower, on average, than in those animals treated with met- or pit-hGH. These results are in accord with those observed clinically, thus supporting the use of the monkey model to predict the relative immunogenicity of some proteins in humans.

An evaluation of in-house and off-the-shelf in silico models: Implications on guidance for mutagenicity assessment

The evaluation of impurities for genotoxicity using in silico models are commonplace and have become accepted by regulatory agencies. Recently, the ICH M7 Step 4 guidance was published and requires two complementary models for genotoxicity assessments. Over the last ten years, many companies have developed their own internal genotoxicity models built using both public and in-house chemical structures and bacterial mutagenicity data. However, the proprietary nature of internal structures prevents sharing of data and the full OECD compliance of such models. This analysis investigated whether using in-house internal compounds for training models is needed and substantially impacts the results of in silico genotoxicity assessments, or whether using commercial-off-the-shelf (COTS) packages such as Derek Nexus or Leadscope provide adequate performance. We demonstrated that supplementation of COTS packages with a Support Vector Machine (SVM) QSAR model trained on combined in-house and public data does, in fact, improve coverage and accuracy, and reduces the number of compounds needing experimental assessment, i.e., the liability load. This result indicates that there is added value in models trained on both internal and public structures and incorporating such models as part of a consensus approach improves the overall evaluation. Lastly, we optimized an in silico consensus decision-making approach utilizing two COTS models and an internal (SVM) model to minimize false negatives.

Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: A case study using aromatic amine mutagenicity

Statistical-based and expert rule-based models built using public domain mutagenicity knowledge and data are routinely used for computational (Q)SAR assessments of pharmaceutical impurities in line with the approach recommended in the ICH M7 guideline. Knowledge from proprietary corporate mutagenicity databases could be used to increase the predictive performance for selected chemical classes as well as expand the applicability domain of these (Q)SAR models. This paper outlines a mechanism for sharing knowledge without the release of proprietary data. Primary aromatic amine mutagenicity was selected as a case study because this chemical class is often encountered in pharmaceutical impurity analysis and mutagenicity of aromatic amines is currently difficult to predict. As part of this analysis, a series of aromatic amine substructures were defined and the number of mutagenic and non-mutagenic examples for each chemical substructure calculated across a series of public and proprietary mutagenicity databases. This information was pooled across all sources to identify structural classes that activate or deactivate aromatic amine mutagenicity. This structure activity knowledge, in combination with newly released primary aromatic amine data, was incorporated into Leadscopes expert rule-based and statistical-based (Q)SAR models where increased predictive performance was demonstrated.

In silico toxicology protocols

The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information.