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Enregistrement W4237143087 · doi:10.17504/protocols.io.vuxe6xn

Guidelines for Validation of Immunogenicity Analysis of Anti-drug Antibodies v1

2018· preprint· en· W4237143087 sur OpenAlexaboutno aff
Bello Smitu

Notice bibliographique

Revuenon disponible
Typepreprint
Langueen
DomaineMedicine
ThématiqueMonoclonal and Polyclonal Antibodies Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBiopharmaceuticalImmunogenicityDrugAntibodyPharmacologyImmunoassayDrug developmentComputational biologyEpitopeMedicineBiologyImmunologyBiotechnology

Résumé

récupéré en direct d'OpenAlex

Abstract: Almost all biopharmaceutical products can cause certain anti-drug antibody (ADA) reactions. Anti-drug antibody reactions may reduce drug efficacy or lead to serious adverse reactions. Anti-drug antibodies usually do not cause significant clinical reactions in humans. However, for some therapeutic proteins, the anti-drug antibody reaction can cause a variety of clinical adverse reactions, including mild events and serious adverse events. Pre-clinical studies have shown that anti-drug antibodies can affect drug exposure, toxicity, pharmacokinetics and pharmacodynamics.Therefore, the immunogenicity of therapeutic proteins has caught the attentionof clinicians, pharmaceutical companies and regulatory agencies. In order to evaluate the immunogenicity of bio-drug molecules and link the experimental results with clinical events, it is necessary to developreliable experimental methods to effectively evaluate the anti-drug antibody response in pre-clinical and clinical studies. Methodological validation is particularly important here, and methodological validation is essential for drug listing applications. Current regulatory documents have limited guidance on the validation of immunoassay methods, especially the lack of guidance on the validation of immunogenic assays. This paper provides scientificsuggestions for the validation of immunoassay methods for anti-drug antibodies. Introduction: Biopharmaceutical products, including amino acid polymers, carbohydrates or nucleic acids, are generally expressed through human cell lines, mammalian cells or bacteria, which are larger than conventional small moleculardrugs (generally larger than 1-3KD). Because of the above characteristics, biopharmaceutical products have greater potential to induce an immuneresponse. Immunogenicity of biopharmaceuticals and intrinsic factors of products (species-specific epitopes, exogenous, glycosylation, degree of aggregation or denaturation, impurities and preparations), external factors (route of administration, chronic or acute administration, pharmacokinetics and endogenous equivalent), patient factors (autoimmune diseases, immunosuppression) It is related to alternative therapy. Anti-drug antibody reactions may lead to severe clinical symptoms, including allergies, autoimmunity and different pharmacokineticcharacteristics. Drug-induced immune response is an important indicator of drug safety and efficacy, which isalso a common concern of regulators, manufacturers, clinicians and patients. Therefore, the Food and Drug Administration of the United States and the regulatorybodies of the European Union, Japan, Canada, Australia and other countries require the evaluation of anti-drug antibodies by pharmacological or toxicological methods. The relationship between immunogenicity and clinical symptoms depends on the objectivedetection and characterization of anti-drug antibodies in preclinical and clinical studies. Therefore, the immunogenic biological analysis method should be properly developed and validated before it can be utilized to detectresearch samples. Existing publications provide guidance onstrategies, methodological development and Optimization for the detection and characterization of anti-drug antibodies. Methodological validation is the evaluation of analytical methods. Specific laboratory methods show that the analytical methods used are suitable for the corresponding detection requirements. Specifically for the detection of anti-drug antibodies, verification means that the method can reliably detect low levels of drug-specific antibodies in complex biological matrices (serum or plasma), such as consistency and repeatability. Methodological validation should be carried out in two stages: the pre-research stage refers to the work before the analysis of samples, the research stage refers to the analysis of samples, and the pre-research stage and the research stage are equally important to show the validity and controllability of analytical methods. Method 1. Anti-drug antibody detection_ Clinical and non-clinical studies usually evaluate the immunogenicity of drugs by detecting and characterizing the anti-drug antibody response induced by treatment. At present, manymethods can be used to detect anti-drug antibodies, including enzyme-linked immunosorbent assay (ELISA), radioimmunoassay (RIA), Radioimmunoprecipitation (RIPA), surface plasmon resonance (SPR), electrochemiluminescence (ECL). Each detection method has its advantages and limitations, which have been discussed inrecent articles. No matter what method is adopted, formal methodological verification should be carried out after the development and optimization of the method to ensure thatthe method is suitable for the corresponding detection requirements. Therefore, it can be predicted that the validation recommendations of this guideline are applicable tomost of the anti-drug antibody immunoassays. Researchers should determine appropriate methodological validation schemes based on the characteristics of the detection system. Anti-drug antibody detection methods are usually non-quantitative (sometimes semi-quantitative) tests, because there is no standardized species-specific anti-drug antibody available as a calibration standard [15]. Positive controls are generally internally developed (e.g. monoclonalantibodies or high immune serum polyclonalantibodies), and it is impossible to use all the anti-drug antibodies detected by subjects as positive controls. If a quality unit is to be used to markthe level of the anti-drug antibody, the parallelism between the standard and the test sample should be proved to determine the accuracy of the concentration of the sample [8,10]. If the parallelism between the sample and the standard is not proved, the accuracy is doubtful. Titration is another method for evaluating antibody levels. It is easy to lackparallelism between dilutions of different samples. However, it is noteworthy that previous studies have shown that titration is suitable for the evaluation of antibody levels. For decades, this method has been usedin clinical infections, diagnosis and treatment of autoimmune diseases, and vaccination. Comparison of Anti-drug Antibody Reactions by Titration, it is simpler and requires less verification because it does not need to describeand prove in detail the parallelism between the anti-drug antibodies of different products and the standard products. 2. Application of Statistics Reducing the subjective role in the verification process is one ofthe key points of this paper. In order to ensurethe objectivity of the experiment, we must rely on statistical means. Since most researchers are unable to obtainthe services of statisticians, this paper provides a simple and sufficiently rigorous and effective statistical method. All the statistical calculations involved can be calculated by commercial statistical software, and no experienced statisticians are needed in the calculation process. However, if statisticians assist in the design of validation experiments and data processing, the application of statistics may be more rigorousand effective than that provided in this paper. 3. Pre research validation Validation tests in clinical and non-clinical studies before sample bioanalysis are called pre-study validation, which mainly describe the performance characteristics of analyticalmethods in mathematical and quantitative aspects [8]. Prevalidationrefers to the preparatory work before the start of the research work, which can notbe confused. On the other hand, research validation refers to monitoring the performance characteristics of the whole process of using the method to ensurethe validity of the method and the reliability of the data. After developing and optimizing the wholemethod, the analysis method should have the condition of verification. For example, optimizing the test data shows that the detection method has potential reliability and is suitable for the corresponding detection requirements. The reliability of the method depends on the normal operation of the analytical equipment and computer system and the proficiency of the experimenters. In essence, analytical methods are a holistic system with multiple factors, not just reagents. The validation method should include system applicability, which belongs to the research validation category. It is suggested thata validation experiment program or SOP should be established before preliminary research and validation. The validation experiment program should explain the expected purpose of the method, the detailed description of the analysis method, the performance characteristics to be validated and the expected acceptance criteria of precision, robustness, stability and durability. In addition, it is suggested that appropriate experimental details and data processing steps should be added to the validation scheme, so as to provide a clearguidance for validatorsto ensure better data processing. The acceptance criteria should be established forthe detection of anti-drug antibodies, which can help to ensure the validity of the test in the research stage. Therefore, the systemapplicability criteria (acceptable range) for quality control should be established after statistical evaluation of the data obtained in the verification process. In addition, there should be acceptance criteria for reference substances and samples in the intermediate precision test during the research and verification stage. When the analysis data in the research and verification stage does not meet the acceptance criteria, the data should be rejected. Acceptance criteria should not be established in the pre-research and verification stage, because it may exclude some validation data, resulting in inaccurate estimation of analysiserrors. In addition to the clearreasons (such as technical errors) and intentional or unintentional deviations from the experimental scheme, all an

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,078
score de la tête « metaresearch » (Gemma)0,071
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Méthodes · Signal consensuel: Méthodes
Score de désaccord entre enseignants0,078
Score d'incertitude au seuil0,412

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0780,071
Méta-épidémiologie (sens strict)0,0030,002
Méta-épidémiologie (sens large)0,0020,003
Bibliométrie0,0100,004
Études des sciences et des technologies0,0030,004
Communication savante0,0040,002
Science ouverte0,0070,003
Intégrité de la recherche0,0100,003
Charge utile insuffisante (le modèle a refusé de juger)0,0110,016

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,135
Tête enseignante GPT0,437
Écart entre enseignants0,302 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreMéthodes

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2018
Routes d'admission1
Résumé présentoui

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