The pharmacokinetic-pharmacodynamics (PK-PD) combined model is an important tool for studying the in vivo process of drugs, the effects of drugs on the body and the relationship between the two, that is, the relationship between dose-concentration and concentration-effect is combined in Research together is more helpful to describe and predict the dose-effect relationship. The use of PK-PD combined model in drug research can provide scientific basis for elucidating the material basis and mechanism of drug efficacy, such as the evaluation of drug effects, the optimization of medication regimens, and the basis for clinical medication. This article briefly reviews the application of the PK-PD model, aiming to provide some theoretical references for expanding its application.
Pharmacodynamics (PD) and pharmacokinetics (PK) are two closely related kinetic processes that proceed simultaneously in the body. However, for a long period of time, PK and PD have been treated separately, and the internal connection between the two has been ignored, which makes the research on PK and PD have certain limitations.
With the continuous in-depth research on PK and PD, people gradually realized this problem, and then proposed a pharmacokinetic-pharmacodynamic (PK-PD) combined model to comprehensively study the dynamic change process of the drug in the body and its efficacy. It uses mathematical methods to quantify the internal relationship between concentration (or dose), time, and effect, which helps to understand the effect of a drug more comprehensively and accurately with dose (or concentration) and time. The law of change has universal guiding significance for the research and development and rational use of drugs.
NCE03) The risk of QTc prolongation. Results NEC01 showed obvious arrhythmia effects. In the expected treatment, the possibility of QT/QTc prolongation caused by NCE02 and NCE03 is considered to be low. It provides a PK-PD basis for transforming the effects of arrhythmia in dogs into humans.
CHAIN et al. evaluated the cross-species translational effects of three compounds known to cause QTc interval prolongation through PK-PD, namely cisapride, d,l-sotalol and moxifloxacin. The results indicate that the PK-PD relationship in dogs may be used as a basis for predicting drug-induced prolongation of human QTc. In addition, the risk of QTc prolongation can be expressed by the probability associated with a threshold greater than or equal to 10 ms, directly inferring the possibility of arrhythmia related to clinically relevant molecules.
HEETLAL et al. established a PK-PD model to measure the concentration of baclofen (ITB) cerebrospinal fluid and evaluate the clinical effects of various ITB boluses on patients with spasticity. All patients achieved sufficient antispasmodic effects when the ITB dose was changed from 50 to 100 mg, and no serious side effects were observed. It is suggested that the concentration and clinical effect of baclofen cerebrospinal fluid (CSF) are significantly related to ITB dose.
HESHAM et al. used nonlinear mixed effects to fit the PK-PD model to evaluate the effect of unfractionated heparin (UFH) in children. The data of 64 children who received 75-100IU·kg-2 UFH during cardioangiography were analyzed. As a result, a linear model of the optimal concentration-effect was obtained, and the relevant covariates can also describe the drug treatment process in vivo.
LOPRETE et al. conducted a population PK-PD analysis of safinamide in patients with Parkinson’s disease and found that clearance and volume of distribution increased with body weight. Age, gender, renal function and exposure to levodopa did not affect safinamide. The pharmacokinetics of fenamide, and safinamide treatment prolonged the prescribing effect by 0.73 h (4th week). The model fully describes the population pharmacokinetics of safinamide and the influence of safinamide on the prescribing effect. There is no need to adjust the dose in the elderly and patients with mild to moderate renal impairment.
Hansson et al. used the PK-PD model to evaluate the adverse drug reactions and overall survival predictors of sunitinib in patients with GIST, and the results showed that hypertension is related to sunitinib exposure. Baseline tumor size, time course of neutropenia, and relative increase in diastolic blood pressure were identified as predictors of overall survival. This proves that the framework can be used for early monitoring of side effects and clinical responses, thereby promoting individualized doses and maximizing overall survival.
BRUSSEE et al. used the PK-PD model to study the effect of L-arginine on endothelial function in patients with moderate to severe falciparum malaria. According to the simulation dose prediction, the duration of the treatment effect will increase with the increase of the arginine dose.
RAMBIRITCH et al. used a population PK-PD model to study the role of glibenclamide in patients with type 2 diabetes in South Africa. The results suggest that the lowest effective dose of glucose is less than 5 mg per day. If the dose exceeds 5 mg per day, the effect of glibenclamide on blood glucose response is meaningless; it also suggests that the effective dose of glibenclamide is lower than 5 mg per day, and the maximum dose of glibenclamide, such as 15 mg per day, Will not further reduce blood sugar levels, but may cause patients to have adverse drug reactions.
BERGES et al. used the PK-PD model of immunohistochemical data from the patient’s muscle biopsy to select the dose of oxalizumab for muscular dystrophy. At the same time, they used a nonlinear mixed-effect method to mimic the rich plasma concentration data and oxaliz Sparse immunohistochemical (IHC) data after intravenous dose of monoclonal antibody. The diagnostic chart indicates that the PK and IHC data results are normal, suggesting that the model can optimize the dose of oxalizumab.
PILLA et al. used the PK-PD model to study the time course of the scores on the three subscales (positive, negative, and general) of the Negative and Positive Symptom Scale (PANSS) after treatment with antipsychotics, and compared atypical antipsychotics (also known as Second-generation antipsychotics) SGAs and typical antipsychotics (first-generation antipsychotics) FGA (haloperidol) control negative symptoms. The results show that to achieve improvement in the PANSS subscale, different D2 or 5-HT2A receptor occupancy levels are required.
The PK-PD modeling method helps distinguish the effects of antipsychotics for different symptoms of schizophrenia, and compared with other non-clozapine SGAs, olanzapine seems to be superior in treating negative symptoms.
MOHAMED et al. used the PK-PD model to confirm that gentamicin has a higher bacterial killing effect on preterm infants than full-term newborns. The interval between gentamicin administrations in preterm infants is extended. For all newborns, 36-48 hours of administration The drug interval is as effective as the 24 h dosing interval of the same total dose.
The PK-PD model of drug-resistant anticancer agent treatment by EIGENMANN et al. proposed that, compared with sensitive cell lines, drug-sensitive tumor cell lines will die or be transformed into drug-resistant cell populations that grow at a slower growth rate. In the simulation study, the selection of resistant cells and the resistance part of time variation to sensitive cells were explored, and the pharmacokinetic process of the emergence of resistance was provided. It is suggested that tumor regrowth during treatment promoted by the selection of drug-resistant cell lines and faster tumor regeneration may occur after tyrosine kinase inhibitor (TKI) treatment is stopped. Finally, it is proposed that the semi-mechanical mode in clinical trials can be used to explore different situations and guide clinical medication.
THORSTED et al. established a mechanical mixed-effect PK-PD model on recombinant human growth hormone (rhGH) in hypophysectomized rats and predicted the relationship between human PK-PD. A PK-PD model of rhGH translation mechanism was successfully developed from experimental data in rats. The model is linked to a clinically relevant biomarker IGF-1, reaching the primary clinical endpoint, growth/weight gain. Scaling model parameters provide a reliable prediction of PK-PD in patients with growth hormone deficiency, including variability.
When Zhang et al. established a PK-PD model to evaluate the antioxidant and anti-inflammatory effects of triterpene ursolic acid (UA), they found that UA is effective in inducing various phase II drug metabolism (DM)/antioxidant genes and inhibiting pro-inflammatory genes in vivo. Effective. This PK-PD modeling method may provide a conceptual framework for the future clinical use of chemopreventive agents in human diets.
YAMAZAKI confirmed that quantitative PK-PD modeling can be used to predict translational pharmacology from non-clinical to clinical through a case study of the anticancer drug crizotinib.
SNELDER et al. used a mechanism-based PK-PD modeling method to prove that only measuring heart rate (HR) and mean arterial pressure (MAP) can quantify the dynamic changes of preclinical cardiovascular safety studies (CVS) and clarify that it has a role The mechanism of action of Dian’s new compound (MoA) is also used to characterize the effects of cardiovascular drugs with different mechanisms of action by establishing a mechanism-based PK-PD model in rats. The results prove that the model can be used to quantify and predict the cardiovascular effects of drugs and clarify the MoA of new compounds, and it can be combined with preclinical data to predict the effects of specific drugs on human blood pressure.
IMA-638 and IMA-026 are humanized IgG1 monoclonal antibodies (mAbs) that target non-overlapping epitopes of IL-13. In order to explain the difference between the two total IL-13 curves and predict the free IL-13 curve of each mAb, TIWARI et al. used a mechanistic PK-PD model to suggest that the elimination of the IL-13-IMA-638 complex is better than that of IL- The 13-IMA-026 complex is about 100 times faster, which may be the reason for the difference in total IL-13 distribution observed in the two mAbs; despite having similar binding KD and PK distributions, IMA-638 is better than equivalent doses of IMA -026 has a stronger and longer-lasting inhibition of free IL-13. In short, the combined model of two similar molecules provides an understanding of the mechanism by which the elimination rate of the mAb-target complex can adjust the degree of inhibition of the free target.
The mechanism-based PK-PD model established by RUZOV et al. used celecoxib and aspirin to bind COX-1 in vitro and judged the interaction between antiplatelet results. The results show that celecoxib can attenuate the antiplatelet effect of low-dose aspirin in vivo to a certain extent. The degree of this interaction can be large.
The PK-PD model is widely used in all stages of new drug development.
YAMAZAKI et al. established a PK-PD model for targeted regulation of tumors and resistance to crizotinib-resistant echinodermal microtubule binding protein-like 4 (EML4)-ALK mutations, EML4-ALKL1196M H3122NSCLC cells in athymic mice The study of the relationship between PF06463922 and PF06471402 of tumor efficacy found that compared with the first-generation anaplastic lymphoma kinase (ALK) inhibitor crizotinib, oral administration of the new small molecules PF06463922 and PF06471402 is the second-generation anaplastic lymphoma kinase ( ALK) inhibitors can target sensitive and resistant non-small cell lung cancer (NSCLC) patients. VOSS et al. confirmed the quantification of the diffusion characteristics of neocortical slices through the PK-PD model.
In addition, PK-PD also has applications in anesthesia, ophthalmology and other fields.
The research of PK-PD model is becoming more and more extensive, and its role in drug development is getting more and more attention. The PK-PD model is a powerful tool to assist drug development, and has a wide range of applications in the evaluation of drug effects, dose adjustment, optimization of dosing regimens, and research on adverse drug reactions. The correct use of the PK-PD model can reduce research and development costs, reduce the probability of experimental failure, and confirm the safety and effectiveness of the drug.
The PK-PD model will continue to be improved with the deepening of research, and will play a more important role in future drug development and research. Ashwagandha A (WA) is one of the main biologically active components of Ashwagandha. It has a wide range of pharmacological activities, such as anti-oxidation, anti-inflammatory, anti-angiogenesis, anti-tumor, anti-microbial and pro-apoptosis. However, there are still few domestic reports on the PK-PD model of WA, and more in-depth studies can be conducted on it in order to provide more evidence for its pharmacological effects.
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