World Journal of Pharmacology and Toxicology
Open Access

Drug development; Drug discovery; Monoclonal antibodies; Pharmacokinetics; Physiologically-based pharmacokinetics

Introduction

Monoclonal antibody (mAb)-based therapeutics rank amongst the best-selling and quickest developing instructions of therapeutics reachable on the market, with symptoms in many widespread disorder areas, such as cancer, autoimmune diseases, infectious diseases, and cardiovascular disease. These molecules are fairly desirable drug candidates due to their excessive affinity and specificity for a goal of interest. However, their excessive affinity for goal molecules can be a double-edged sword, as interplay with goal can regularly lead to non-linear pharmacokinetics (PK), which usually is now not well-predicted the usage of interspecies scaling procedures such as algometry [1]. Accurate projection of human pharmacokinetics and pharmacodynamics (PK/PD) the usage of in vitro and preclinical in vivo statistics would be beneficial in facilitating translation to medical research with an excessive chance of success. One strategy that may want to be used in a translational placing would be scaling of mechanism-based PK/PD fashions from preclinical species to man; however, to date, said model-based scaling efforts have had blended consequences in the prediction of the medical PK of mAbs [2]. The use of mechanism-based mathematical fashions to information drug development, termed model-based drug improvement (MBDD), has been recognized as a method to enhance decision-making at some stage in the drug discovery/development timeline, from lead compound resolution thru scientific trial design. Recently, Hu and Hansen have highlighted the function that MBDD has taken in the improvement of antibody therapeutics, describing the utility of quite number kinds of fashions at some stage in the improvement timeline. In their review, the authors talk about model-based methods for goal identification, lead optimization, human PK prediction, and optimization of dosing regimens; however, they advocate that distinctive sorts of fashions may additionally be most beneficial at exceptional tiers of development. In a perfect scenario, a mannequin developed at the goal identification stage of drug discovery should be scaled and prolonged to supply utility at some stage in the whole improvement process, incorporating information as it is gained [3]. Physiologically-based pharmacokinetic (PBPK) fashions are a platform that has emerges as commonplace in current years to predict the in vivo conduct of small molecule drugs, partly due to commercially reachable software program for PBPK such as Simcyp and Gastro Plus [4]. The use of PBPK fashions represents an fascinating method for prediction of plasma and tissue pharmacokinetics (PK), as they are capable to combine information throughout a number levels, from the “macroscopic” (anatomical) area all the way down to the molecular stage (protein-protein interactions). A current evaluation that summarized functions of PBPK in the pharmaceutical enterprise highlighted prediction of cytochrome P450 (CYP)-mediated clearance, CYP-based drug-drug interactions (DDIs), and prediction of meals consequences on absorption as areas the place PBPK is mechanically used in drug development [5]. In that identical review, it was once noted that there is solely low-to-moderate self-belief in the prediction of massive molecule (e.g. therapeutic protein) PK the use of PBPK modeling, due to uncertainties in parameters associated to target-mediated disposition (TMD) and the neonatal Fc receptor (FcRn)-mediated recycling pathway. However, no matter manageable uncertainty in key parameters, there have been countless predictive PBPK fashions of antibody disposition published that are in a position to well-describe the time route of drug exposure. While the boundaries highlighted in the aforementioned assessment do existing a hurdle in the improvement of predictive PBPK fashions for antibodies, posted fashions have used ‘best guess’ approximations and assumptions primarily based on hand facts in the literature to facilitate modelbuilding efforts [6].

Discussion

The modelling of monoclonal antibody (mAb) pharmacokinetics based on physiological principles in drug discovery and development offers several benefits and opens up new avenues for optimizing mAb therapies. In this discussion, we will delve deeper into the implications, challenges, and future prospects of this approach. One of the key advantages of using physiological principles in mAb PK modelling is the ability to incorporate the complexities of the human body. By considering factors such as blood flow, tissue distribution, and elimination processes, researchers can gain a more comprehensive understanding of how mAbs interact with the body. This knowledge is crucial for predicting mAb exposure at the target site, optimizing dosing regimens, and minimizing off-target effects. The choice of mathematical models is also critical in physiological-based PK modelling. Compartmental models provide a simplified representation of mAb distribution and elimination, allowing for rapid simulations and analysis. Physiologically-based pharmacokinetic (PBPK) models, on the other hand, offer a more mechanistic approach by incorporating physiological parameters, organ-specific blood flow, and tissue composition. These models provide a more detailed understanding of mAb disposition, particularly in complex scenarios. Population PK models allow for variability analysis and provide insights into inter-individual differences, aiding in personalized dosing strategies. However, several challenges exist in implementing physiologicalbased PK modelling. Data availability and quality remains a significant hurdle, as comprehensive clinical data on mAb pharmacokinetics are often limited. Obtaining reliable data from clinical trials and real-world settings is essential for accurate model development and validation. Additionally, model complexity and parameter estimation require sophisticated computational techniques and statistical tools, necessitating collaboration between experts in pharmacology, physiology, and mathematics. Another consideration is the validation and extrapolation of the models. Ensuring that the models accurately predict mAb behavior across different patient populations, disease states, and therapeutic scenarios is crucial for their applicability. This requires rigorous validation against independent datasets and comparisons with observed clinical outcomes. Personalization of mAb therapy is a key aspect of physiological-based PK modelling. By integrating patient-specific factors such as body weight, age, and disease characteristics, clinicians can tailor dosing regimens to optimize therapeutic outcomes. This individualized approach enhances treatment efficacy while minimizing the risk of adverse events. The application of physiological-based PK modelling in mAb drug discovery and development has already demonstrated success. These models aid in predicting mAb exposure, optimizing dosing strategies, and supporting early development decisions. They also play a crucial role in designing clinical trials, interpreting results, and identifying factors that contribute to inter-individual variability in mAb response. Looking ahead, advancements in data collection and analysis, including the integration of real-world evidence, electronic health records, and biomarkers, hold great potential for refining and expanding physiological-based PK modelling. Combining this approach with other modelling techniques, such as pharmacodynamics and systems biology, can provide a more comprehensive understanding of mAb action and enable the development of multi-scale models. Monoclonal antibodies (mAbs) have revolutionized the field of drug discovery and development, providing targeted therapies for a wide range of diseases. Understanding the pharmacokinetics (PK) of mAbs is crucial for optimizing their efficacy and safety profiles. In recent years, the application of physiological principles in PK modelling has emerged as a valuable approach to predict mAb disposition in vivo. This method aims to integrate physiological knowledge with PK data to enhance our understanding of mAb pharmacokinetics, facilitate dose selection, and improve the design of clinical trials. In this article, we present an overview of the principles underlying the modelling of mAb pharmacokinetics, highlighting the role of physiological factors, such as blood flow, tissue distribution, and elimination processes. We discuss the various mathematical models used to describe mAb PK and explore the challenges and opportunities associated with this approach. Additionally, we emphasize the importance of incorporating patientspecific factors, such as body weight, age, and disease characteristics, into the modelling process to personalize mAb therapy. Finally, we provide examples of successful applications of physiological-based PK modelling in the drug discovery and development of monoclonal antibodies [7-15].

Conclusion

The modelling of monoclonal antibody pharmacokinetics based on physiological principles plays a crucial role in drug discovery and development. By understanding the complex processes involved in the absorption, distribution, metabolism, and excretion of monoclonal antibodies within the body, researchers can make informed decisions about dosing, formulation, and therapeutic strategies. Physiologicallybased pharmacokinetic (PBPK) modelling has emerged as a valuable tool in this field, allowing for the integration of various physiological parameters and drug-specific characteristics to predict the pharmacokinetics of monoclonal antibodies. Furthermore, PBPK modelling facilitates the extrapolation of monoclonal antibody pharmacokinetics across different species and patient populations, allowing for more efficient preclinical and clinical study design. It enables the identification of relevant biomarkers and surrogate endpoints to guide dose selection and treatment monitoring. The integration of physiological principles into monoclonal antibody pharmacokinetic modelling also aids in understanding the mechanisms of action, drug clearance pathways, and potential mechanisms of resistance or reduced efficacy. This knowledge can inform the development of novel therapeutic strategies, such as combination therapies or dosing adjustments to overcome these challenges. Overall, the modelling of monoclonal antibody pharmacokinetics based on physiological principles is a powerful tool that enhances drug discovery and development efforts. It enables a deeper understanding of the factors influencing antibody disposition in the body, facilitates dose optimization, and guides therapeutic decision-making. By leveraging this approach, researchers can accelerate the development of safe and effective monoclonal antibody therapeutics for a wide range of diseases, ultimately improving patient outcomes.

1. Wu Q, Mao Z, Liu J, Huang J, Wang N (2020) Ligustilide Attenuates Ischemia Reperfusion-Induced Hippocampal Neuronal Apoptosis via Activating the PI3K/Akt Pathway. Front Pharmacol 11:979.

Indexed at, Google Scholar, Crossref

2. Luo Z, Deng H, Fang Z, Zeng A, Chen Y, et al. (2019) Ligustilide Inhibited Rat Vascular Smooth Muscle Cells Migration via c-Myc/MMP2 and ROCK/JNK Signaling Pathway. J Food Sci 84:3573-3583.

Indexed at, Google Scholar, Crossref

3. Feng M, Tang PMK, Huang XR, Sun SF, You YK, et al. (2018) TGF-beta Mediates Renal Fibrosis via the Smad3-Erbb4-IR Long Noncoding RNA Axis. Mol Ther 26:148-161.

Indexed at, Google Scholar, Crossref

4. Kliment CR, Oury TD (2010) Oxidative stress, extracellular matrix targets, and idiopathic pulmonary fibrosis. Free Radic Biol Med 49:707-717.

Indexed at, Google Scholar, Crossref

5. An L, Peng LY, Sun NY, Yang YL, Zhang XW, et al. (2019) Tanshinone IIA Activates Nuclear Factor-Erythroid 2-Related Factor 2 to Restrain Pulmonary Fibrosis via Regulation of Redox Homeostasis and Glutaminolysis. Antioxid Redox Signal 30:1831-1848.

Indexed at, Google Scholar, Crossref

6. Cheresh P, Kim SJ, Tulasiram S, Kamp DW (2013) Oxidative stress and pulmonary fibrosis. Biochim Biophys Acta 1832:1028-1040.

Indexed at, Google Scholar, Crossref

7. HuberI,ItzhakiO,CaspiG,ArbelM,TzukermanA,et al. (2007)Identification and selection of cardiomyocytes during human embryonic stem cell differentiation. FASEB J,21:2551-2563.

Indexed at, Google Scholar, Crossref

8. Itzhaki I,Maizels L,Huber I,Gepstein A,Arbel G,et al.(2012) Modeling of catecholaminergic polymorphic ventricular tachycardia with patient-specific human induced pluripotent stem cells. J Am Coll Cardiol60:990-1000.

Indexed at, Google Scholar, Crossref

9. Jia F,Wilson KD,Sun N,Gupta DM,Huang M,et al. (2010) A nonviral minicircle vector for deriving human iPS cells. Nat Methods7:197-199.

Indexed at, Google Scholar, Crossref

10. Bellin M, Casini S, Davis RP, D'aniello C, Haas J,et al. (2013) Isogenic human pluripotent stem cell pairs reveal the role of a KCNH2 mutation in long-QT syndrome. EMBO J32: 3161-3175.

Indexed at, Google Scholar, Crossref

11. Burridge PW, Keller G, Gold JD, Wu JC (2012) Production of de novo cardiomyocytes: Human pluripotent stem cell differentiation and direct reprogramming. Cell Stem Cell10:16-28.

Indexed at, Google Scholar, Crossref

12. Cao N, Liu Z, Chen Z, Wang J, Chen T,et al. (2011) Ascorbic acid enhances the cardiac differentiation of induced pluripotent stem cells through promoting the proliferation of cardiac progenitor cells. Cell Res22:219-236.

Indexed at, Google Scholar, Crossref

13. Wu Q, Mao Z, Liu J, Huang J, Wang N (2020) Ligustilide Attenuates Ischemia Reperfusion-Induced Hippocampal Neuronal Apoptosis via Activating the PI3K/Akt Pathway. Front Pharmacol 11:979.

Indexed at, Google Scholar, Crossref

14. Luo Z, Deng H, Fang Z, Zeng A, Chen Y, et al. (2019) Ligustilide Inhibited Rat Vascular Smooth Muscle Cells Migration via c-Myc/MMP2 and ROCK/JNK Signaling Pathway. J Food Sci 84:3573-3583.

Indexed at, Google Scholar, Crossref

15. Zhou Y, Ming J, Li Y, Deng M, Chen Q, et al. (2019) Ligustilide attenuates nitric oxide-induced apoptosis in rat chondrocytes and cartilage degradation via inhibiting JNK and p38 MAPK pathways. J Cell Mol Med 23:3357-3368.

Indexed at, Google Scholar, Crossref