Gennate, Ltd., 71-75 Shelton Street, London, WC2H9JQ, UK.
Clin Pharmacokinet. 2022 Jul;61(7):921-927. doi: 10.1007/s40262-022-01149-8. Epub 2022 Jul 12.
In a typical course of drug development, thorough pharmacokinetic (PK) studies are essential for the determination of drug biodistribution, dosage and efficacy without toxicity. For vaccines, however, unless a new formulation component is used, most regulatory agencies rule out the need for studying the biodistribution of the vaccine antigenic material per se, and only dose-immunogenicity studies are performed. This is because traditional vaccines are meant to directly induce immunogenicity by locally recruiting immunocytes that will carry on with the pursuing immunogenic processes. Thus, the clinical outcome from traditional vaccines is determined mainly by an immunological response phase. Yet, the case is significantly different for the emergent genetic vaccines (vectorised DNA or mRNA vaccines), where the clinical outcome is dependent on a combination of two major response phases: a pharmacological phase that involves biodistribution, assimilation, gene translation and epitope(s) presentation, followed by an immunological phase, which is similar to that of traditional vaccines. From a mathematical perspective, processes involved in drug administration are typically subject to inter- and intra-patient statistical distributions like most physiological processes. Therefore, the clinical outcome after administering genetic vaccines obeys a statistical probability distribution combined of the sum of two major response probability distributions, pharmacological and immunological. This implies that the variance coefficient of the summed response probability distributions has a larger value than the variance of each underlying distribution. In other words, due to the multi-phased mode of action of genetic vaccines, their clinical outcome has more variability than that of traditional vaccines. This observation points toward the necessity for regulating genetic vaccines in a similar manner to bio-therapeutics to ensure better efficacy and safety. A structural PK model is provided to predict the sources of variability, biodistribution and dose optimisation.
在药物开发的典型过程中,透彻的药代动力学(PK)研究对于确定药物的生物分布、剂量和疗效而无毒性是必不可少的。然而,对于疫苗而言,除非使用新的配方成分,否则大多数监管机构规定无需研究疫苗抗原物质本身的生物分布,而仅进行剂量-免疫原性研究。这是因为传统疫苗旨在通过局部招募免疫细胞来直接诱导免疫原性,这些免疫细胞将继续进行免疫原性过程。因此,传统疫苗的临床结果主要取决于免疫反应阶段。然而,对于新兴的基因疫苗(载体化 DNA 或 mRNA 疫苗),情况则大不相同,其临床结果取决于两个主要反应阶段的结合:一个是药理学阶段,涉及生物分布、吸收、基因翻译和表位呈现,其次是免疫阶段,类似于传统疫苗。从数学的角度来看,药物给药过程中涉及的过程通常与大多数生理过程一样,受到个体内和个体间的统计分布的影响。因此,给予基因疫苗后的临床结果遵循由两个主要反应概率分布(药理学和免疫学)的总和组成的统计概率分布。这意味着,总和反应概率分布的方差系数大于每个基础分布的方差。换句话说,由于基因疫苗的多阶段作用模式,其临床结果比传统疫苗更具可变性。这一观察结果表明,需要以类似于生物治疗药物的方式来监管基因疫苗,以确保更好的疗效和安全性。提供了一个结构 PK 模型来预测变异性、生物分布和剂量优化的来源。