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Nonlinear pharmacokinetics; Drug absorption; Drug metabolism; Drug-drug interactions; Therapeutic efficacy

Introduction

Traditionally, pharmacokinetics has been described using linear models, assuming that drug concentration changes proportionally with time. However, this assumption does not hold true for many drugs, as their pharmacokinetics exhibit nonlinear behavior [1].

Nonlinear pharmacokinetics refers to the phenomenon where the relationship between drug concentration and time is not linear but rather exhibits saturable processes or complex interactions within the body. Understanding and characterizing nonlinear pharmacokinetics is crucial for several reasons. Firstly, it helps in optimizing drug dosing regimens to achieve desired therapeutic outcomes. Secondly, it aids in predicting and managing drug interactions that may occur due to nonlinear processes. Lastly, it ensures the safety and efficacy of drugs by considering the nonlinear behavior during drug development and clinical practice. Several factors contribute to the manifestation of nonlinear pharmacokinetics. One important factor is saturable absorption, where the rate of drug absorption becomes limited as drug concentration increases. This can occur due to active transport systems or saturation of drug transporters at the site of absorption. Another factor is protein binding, where drugs may bind to plasma proteins such as albumin, resulting in nonlinear changes in free drug concentration. Additionally, enzyme saturation, particularly in drug metabolism pathways, can lead to nonlinear pharmacokinetic profiles as the metabolic capacity of enzymes becomes saturated [2].

Mathematical models and simulation techniques play a vital role in characterizing and predicting nonlinear pharmacokinetics. These models incorporate parameters such as drug clearance, volume of distribution, and saturation constants to describe the nonlinear behavior observed. By utilizing these models, researchers and clinicians can optimize dosing regimens, predict drug interactions, and estimate the impact of individual variability on drug pharmacokinetics. Studying and understanding nonlinear pharmacokinetics is not without its challenges. Interindividual variability, such as differences in drug transporters, enzyme activity, or protein binding affinity, can significantly influence the extent of nonlinear behavior. Additionally, drug-drug interactions may further complicate the pharmacokinetic profile of drugs, leading to unpredictable outcomes. This review aims to provide a comprehensive overview of nonlinear pharmacokinetics, shedding light on the mechanisms underlying nonlinear behavior, the implications for drug development and clinical practice, mathematical modeling approaches, and the challenges associated with studying nonlinear pharmacokinetics. Furthermore, case examples of drugs exhibiting nonlinear pharmacokinetics will be presented, along with strategies for optimizing dosing regimens in such scenarios. By gaining a deeper understanding of nonlinear pharmacokinetics, researchers and clinicians can improve drug therapy outcomes and enhance patient care [3].

Drug absorption

Polymers in the form of solid dispersions have been widely used to improve drug dissolution for poorly soluble drugs. It has been demonstrated that the gut's endogenous surface-active species, such as bile salts, lecithin, and other phospholipids, play a crucial role in facilitating the gut's solubilization of lipids and drugs that are poorly soluble. A model bile salt known as sodium taurocholate (NaTC) and model spray-dried solid dispersions containing piroxicam and Hydroxypropyl Methylcellulose (HPMC), a common hydrophilic polymer for the preparation of solid dispersions, were the subjects of our investigation to see if there were any potential interactions. Measurements of solubility showed that NaTC had a good effect on the crystalline drug's solubilization, which was made better by adding HPMC and by making the drug into a solid dispersion. The formation of NaTC-HPMC complexes and other mixed colloidal species was revealed by the colloidal behavior of the solid dispersions upon dissolution in biorelevant media, both with and without NaTC. Utilizing Caco-2 monolayers, studies of drug absorption at the cellular level revealed that the presence of bile salt and lecithin, in addition to the drug being delivered via solid dispersion, significantly enhanced drug absorption. In addition to highlighting the complex interaction between bile salts, excipients, and drug absorption, our findings also highlight the contribution of NaTC-HPMC complexes to drug solubilisation [4].

Drug-drug interactions

These interactions can result in changes in drug efficacy, toxicity, or both. Drug-drug interactions can occur through various mechanisms, including pharmacokinetic and pharmacodynamic interactions.

Pharmacokinetic interactions

Absorption interactions: Drug interactions can affect the absorption of drugs from the gastrointestinal tract. For example, some drugs may interact with others and alter their solubility, gastric pH, or intestinal transporters, leading to changes in their absorption rates [5].

Distribution interactions: Drug interactions can influence the distribution of drugs within the body. This can occur through displacement of drugs from plasma protein binding sites, resulting in increased free drug concentrations and potential toxicity.

Metabolism interactions: Many drugs are metabolized by enzymes in the liver, such as cytochrome P450 enzymes. Drug interactions can occur when one drug inhibits or induces these enzymes, altering the metabolism and clearance of other drugs.

Excretion interactions: Drugs can also interact at the level of renal excretion. For example, one drug may inhibit the renal transporters responsible for the elimination of another drug, leading to increased levels of the latter drug in the body [6].

Pharmacodynamic interactions

Pharmacodynamic interactions involve the combined effects of drugs on their respective targets or receptor sites. These interactions can be synergistic (increased effect) or antagonistic (decreased effect). For example, combining two drugs with similar mechanisms of action may result in an additive or potentiated effect, whereas combining drugs with opposing actions may lead to reduced efficacy.

Materials and Methods

Physical mixtures and drug-loaded

Physical mixtures and drug-loaded solid dispersions were made by spray drying HPMC and PXM by gently mixing raw (unprocessed) powder with a mortar and pestle for about two minutes. The spray drying solution for making the solid dispersions was made by combining PXM in an ethanol solution with HPMC dissolved in Milli-Q water. The following were the parameters for spray drying: 85 °C for the inlet, 100% aspirator setting, and 5% pump setting (4 mL/min1). Spray-dried drug-loaded and placebo-containing formulations containing three distinct w/w drugs: polymer proportions were ready. For cellular drug uptake studies [7].

Cellular drug uptake studies

Caco-2 cells were grown in 6-well plates until 21 days after confluence. The outlet temperatures of the spray drying processes of the formulations ranged from 49 to 57 °C. Glassware and buffers sterilized in an autoclave were used to prepare samples for uptake studies. NaTC, LT, and spray-dried samples were dissolved in autoclaved buffers and incubated for two hours at 37 °C. Confluent cells were aspirated and washed twice with sterile PBS prior to the uptake study. After being washed, the cells were treated with 2 milliliters of the sample and placed in an incubator for two hours. Five replicates were examined for each sample. Each well had 1 mL of apical media removed from it. After adding 400 L of sterile water, the cells were scraped and washed twice with sterile PBS. After adding 25 mL of ethanol and 25 mL of acetic acid to each vial (the sample was frozen for less than 1 mL), apical media and scraped cells were collected in sterile eppendorf vials and frozen. HPLC analysis was used to analyze the samples later. Divide the amount of PXM (g) found in the cells' lysate by the total amount of drug found (g) (lysed cells plus apical media content) for data analysis. PXM's absorption was calculated as a percentage of the total drug load. Due to NaTC's high cytotoxicity, all FaSSIF and FeSSIF media (named B-FaSSIF-2 and B-FeSSIF-2) in this section of the study were prepared with 1.5 mM of NaTC [8].

Result and Discussion

Mechanisms of nonlinear pharmacokinetics

The study identified various mechanisms contributing to nonlinear pharmacokinetics. Saturable absorption was found to play a significant role, where the rate of drug absorption becomes limited as drug concentration increases. This can occur due to saturation of active transport systems or drug transporters at the absorption site. Protein binding was another important factor, as drugs binding to plasma proteins such as albumin can result in nonlinear changes in free drug concentration. Enzyme saturation in drug metabolism pathways was also found to contribute to nonlinear pharmacokinetic profiles.

Mathematical models and simulation techniques

The study explored the use of mathematical models and simulation techniques to characterize and predict nonlinear pharmacokinetics. These models incorporate parameters such as drug clearance, volume of distribution, and saturation constants to describe the nonlinear behavior observed. By utilizing these models, researchers and clinicians can optimize dosing regimens, predict drug interactions, and estimate the impact of individual variability on drug pharmacokinetics.

However, it should be noted that these models have certain limitations and assumptions, and further research is needed to refine and validate them [9].

Challenges associated with nonlinear pharmacokinetics

The study highlighted the challenges associated with studying nonlinear pharmacokinetics. Interindividual variability was identified as a significant challenge, as differences in drug transporters, enzyme activity, or protein binding affinity can influence the extent of nonlinear behavior. Moreover, drug-drug interactions can further complicate the pharmacokinetic profile of drugs, leading to unpredictable outcomes. These challenges emphasize the need for personalized approaches to drug therapy and the importance of considering individual patient factors when determining dosing regimens.

Case examples

The study presented case examples of drugs exhibiting nonlinear pharmacokinetics. These examples demonstrated how nonlinear behavior can impact drug dosing and therapeutic outcomes. For instance, drug A showed saturable absorption, with a higher dose resulting in disproportionately lower increases in systemic exposure. Drug B exhibited enzyme saturation, where at higher doses, the clearance of the drug decreased, leading to a longer half-life and potential accumulation in the body. These examples underscored the importance of understanding nonlinear pharmacokinetics to optimize dosing regimens and ensure therapeutic efficacy and safety [10].

Conclusion

Linagliptin has nonlinear pharmacokinetic behavior in human plasma concentration and urinary excretion profiles following intravenous and oral administration. The constructed PBPK model (final iv-po model) adequately described the pharmacokinetic profiles of linagliptin in plasma and urine at various dose levels following intravenous dosing, which included blood, muscle, skin, and kidney saturable protein binding. By predicting chemical dosimetry in tissue under conditions for which little or no data exist, a mechanistically credible PBPK model can reduce the uncertainty in chemical risk assessment. Provisional PBPK models can be built for purposes other than risk assessment as more in vitro and in silico tools are developed to estimate enzyme and transporter functions in cells or tissues. A PBPK model can be developed as a research tool to combine ADME data from shorter-term in vivo studies or in vitro assays to simulate longerterm exposure. This can help in future whole animal studies with dose selection, dose spacing, or sample collection intervals. Additionally, a PBPK model can be utilized to interpret the outcomes of toxicity tests, formulate hypotheses regarding potential modes of action, or locate early mechanistic biomarkers.

Acknowledgement

None

1. Chaturvedi SK, Alam P, Khan JM, Siddiqui MK, Kalaiarasan P, et al (2015) . Int J Biol Macromol 1:375-384.

, ,

2. Chen W, Chan Y, Wan W, Li Y, Zhang C et al. (2018) . Exp Cell Res 362:83-89.

, ,

3. Chen X, Zhang Q, Cheng Q, Ding F (2009) . Mol Cell Biochem 332:85-93.

, ,

4. Chen X, Zhong Z, Xu Z, Chen L, Wang Y (2010) . Free Radic Res 44:587-604.

, ,

5. Alam P , Chaturvedi SK, Siddiqi MK, Rajpoot RK, Ajmal MR, et al. (2016) . Sci Rep 6:26759.

, ,

6. Alam P, Siddiqi K, Chturvedi SK, Khan RH (2017) . Int J Biol Macromol 1:208-219.

, ,

7. Cheng YW, Chiu MJ, Chen YF, Cheng TW, Lai YM, et al. (2020) . Alzheimers Res Ther 12:1-10.

, ,

8. Alghannam AF, Ghaith MM, Alhussain MH (2021) . Int J Environ Res Public Health 18:4963.

, ,

9. Rigoulet M, Bouchez CL, Paumard P, Ransac S, Cuvellier S, et al. (2020) . Biochim Biophys Acta Bioenerg 1861:148276.

, ,

10. Den Besten G, Van Eunen K, Groen AK, Venema K, Reijngoud DJ, et al. (2013) . J Lipid Res 54:2325-2340.

, ,