Saliva Sampling in Therapeutic Drug Monitoring and Physiologically Based Pharmacokinetic Modeling: Review

Abstract

Therapeutic drug monitoring investigations based on saliva samples can be utilized as an alternative to blood sampling for many advantages. Moreover, the development of physiologically based pharmacokinetic (PBPK) modeling tools can further help to estimate drug exposure from saliva. This review discusses the use of saliva samples and illustrates the applications and examples of PBPK modeling systems for estimating drug exposure from saliva.

Key words

TDM - pharmacokinetics - salivary excretion classification system (SECS) - (PBPK) modeling - Drug absorption - Narrow therapeutic index - Dried saliva spots (DSSs)

Introduction

The efficacy and safety of treatments implemented play an important role in determining the overall health of populations. Many factors can affect drug pharmacokinetics such as physiological factors, coadministration of other medications and environmental variables; therefore, monitoring the administered drug's concentrations enhances treatment effectiveness and safety. Therapeutic drug monitoring (TDM) is detecting concentrations of a drug in a biological fluid at a single or several periods following a drug intake for adjusting and customizing drug dosage and administration. TDM helps to anticipate a patient's response and to choose the ideal drug dosage for initiating and sustaining a clinical response [1].

Drugs' efficacy and safety can be anticipated if the medication level maintains within the therapeutic range. Though not all medications need to be monitored, TDM is critical when assessing medications with a narrow therapeutic window between the lethal and the therapeutic dose. TDM is also essential for highly variable drugs to prevent toxic high concentration levels. Moreover, abnormal response to treatment, unusual adverse events, suspected misuse, and lack of adherence are among the most common TDM applications.TDM is also a significant practice for a specific population (such as pregnant women, children, elderly, and obese people) or some disease status that can affect the drug pharmacokinetics [2]. TDM involves measuring the drug concentration and/or its primary metabolites in body fluids (such as plasma, serum, saliva and urine).

This review provides an overview of TDM using saliva samples and shows the latest advanced applications of physiologically based pharmacokinetic modeling systems in estimating drug exposure from saliva.

TDM saliva-based sample

The main TDM applications for the majority of systemic drugs are performed in the blood (or plasma). TDM with blood sampling is well established and widely used approach. However, due to pain, anxiety, risk of infection, restricted blood supply and limited accessible veins in some populations, extensive blood sampling can be unpractical [3]. Saliva testing is a simple and safe procedure that can be used as an excellent substitute for serum testing to assess drug exposure as most substances identified in the blood are also present in saliva, including DNA, RNA, and protein. Because of its non-invasive nature, lack of expert training or equipment requirement, low cost, ease of analysis, and patient compliance it has become a valuable clinical approach to determining the drug's concentration and may have a stronger predictive value for toxicity and clinical prognosis [3]. Diabetes, cardiovascular disease, dental caries, inflammation and other oral disorders also have all been identified using saliva test biomarkers [4] [5] [6] [7]. Saliva sampling can be used to monitor drugs for neonates, who usually show more variation in drug exposure, drug efficacy, and drug toxicity TDM [8]. Saliva sampling can be also valuable in a variety of settings and clinically challenging conditions such as elderly and anxious patients. Several studies in different age individuals have evaluated the usage of saliva sampling for TDM, and it has been demonstrated to be an effective method, especially for narrow therapeutic window drugs such as artemisinin, digoxin, lamotrigine, phenytoin, carbamazepine, phenobarbital, voriconazole, tacrolimus and lithium[8] [9] [10] [11] [12] [13]. Published studies have described the relationships between saliva and plasma samplings for monitoring antiepileptic drugs levels such as clobazam, ethosuximide, gabapentin, lacosamide, levetiracetam, oxcarbazepine, primidone, topiramate, and zonisamide [14]. Measuring these drugs levels in the oral fluid of epileptic patients who use chronic medication gives a more accurate indication of the pharmacodynamically active, free quantities of these chemicals in serum [15]. TDM of antibiotics such as moxifloxacin, linezolid, gentamicin, and azithromycin using saliva-based samplings have also been also investigated and shown that the drug levels of these samples were comparable to serum [16] [17] [18].

Factors affect TDM in saliva

Age, sex, and food all have an impact on saliva volume and composition, which can vary from person to person. Patients' levels of hydration are hypothesized to affect parotid salivary flow rates, which in turn affect medication concentrations in saliva [19]. Since most of the saliva is water, it is assumed that a decrease in water volume caused by dehydration would result in a loss in salivary production. Moreover, several factors can influence the diffusion of a drug in saliva. The pH of saliva and the acid dissociation constant (pKa) of the drug can affect the drug level in saliva samples as presented for amphetamine saliva sampling where the paired saliva-to-serum concentrations of amphetamine can be very variable and greatly influenced by salivary pH [20]. The lipophilicity and protein binding affinity characterize of the drug can influence the TDM in saliva. The Salivary Excretion Classification System (SECS) was developed following the drug's physicochemical characteristics [18]. This method divides medications into four types based on intestinal permeability and protein binding. Using these two metrics as primary criteria, one can predict medications that are suitable to be monitored using saliva samples. According to SECS, class I and II medications with poor protein binding are subjected to salivary excretion. Class III medication with a high protein binding and high intestinal permeability, is susceptible to salivary excretion because of its low proportion of unbound and high permeability. Class IV medicines with high protein binding and limited intestinal permeability are not expected to be excreted in the saliva [18]. SCECS approach can be valuable using in-silico tools in the early stage of drug discovery, which can predict drug characters and expect drugs that can be monitored in salivary secretion.

Sample collection and analysis method

Saliva samples can be collected directly, passively through drooling, or with the aid of special equipment [11]. A few difficulties could arise with the method of sampling collection as variation in recovery depending on the kind of cotton rolls utilized has been documented, which can absorb the collected saliva samples. Therefore, it is important to standardize and tightly control the process of collecting saliva samples [10]. Also, it is highly advised to assess the matrix impact of collecting tubes as studies showed that serum separator gel-filled tubes can significantly impact the determination of some medications [10]. Additionally, samples taken by spitting have a higher bacterial content than samples taken by drooling, which may impact the results. The passive drooling technique is seen to be a promising substitute with significant amounts of saliva that may be gathered in a short time. For an immediate analysis, specimens can be kept at room temperature for a maximum of 30–90 minutes. To stop bacterial development and additional salivary molecule destruction, specimens can be kept at 4°C for no more than 6 hours. Moreover, the saliva samples following saliva collection can be preserved and maintained frozen at −20°C for years till the time of the drug analysis [4].

The method for obtaining a dried biological fluid is known as dried matrix spots (DMSs) sampling. It involves spotting the liquid specimen onto a collection card, allowing it to dry, and then transferring it to a tube with an extracting solvent [15] [21]. Likewise, Dried saliva spots (DSSs) are reported in the literature as an alternative to liquid oral fluid for TDM [22]. DSSs method offers many benefits, including simple transit and storage, cheap shipping costs, a tiny volume of greater analyte stability, a sample, and less chance of contamination because of an improperly handled sample [23]. Detecting and quantifying drugs of abuse in the toxicological analysis might be done more quickly and affordably by using DSSs [23].

The efficiency of saliva sample preparation and analysis is improved by combining the DSS method with a highly sensitive detection instrument [22] [24]. The assay protocols used for drug testing have been performed by applying a range of analytical techniques, including high-performance liquid chromatography (HPLC) techniques, solid-phase extraction (SPE), liquid-liquid extraction (LLE), and protein precipitation (PP) approaches[21]. Gas chromatography coupled to mass spectrometry (GC-MS) and ultra-high-performance liquid chromatography (UHPLC) coupled to MS/MS, liquid chromatography (LC) coupled to tandem mass spectrometry (MS/MS) have also been commonly used [19]

PBPK and saliva sampling

The physiologically based pharmacokinetic is one of the recognized modeling that has a significant aid in the optimal design of pharmaceuticals drug [25]. PBPK modeling and simulation is a tool for predicting drug pharmacokinetics and assessing the properties of intrinsic (e. g., organ dysfunction, age, genetics polymorphism, or disease state) and extrinsic (e. g., drug-drug interactions (DDI) and drug-food interactions) factors [25] [26] [27]. The processes of drug absorption, distribution, metabolism, and excretion are described by a set of differential equations in PBPK models, which reflect the physiology of the body compartments. Simulations range from a simple system with only a few critical compartments to full-body PBPK models with compartments connected by blood flow that represent all main organs in the body. In this approach, physiochemical parameters from different sources such as in silico predictions, in vitro or in vivo experiments can be combined to predict drug absorption, disposition, and excretion of substances for various dosing regimens in different species, populations, or disease states [25] [27] [28] [29]. Thus, this approach can be valuable in individualized treatment as parameters can be computationally fitted in for specific disease/ population models to best describe in vivo drug concentrations and mechanistically understand the change in absorption. Moreover, this approach can predict the transporters' induction levels, inhibition, and the influence of DDI in each tissue, for example, glycoprotein (Pgp), cytochrome P450 (CYP), organic anion transporting polypeptide (OATP) and multidrug resistance protein transporters [30] [31] [32].

Although a valid prediction of population PK parameters requires very comprehensive pharmacokinetic data, this approach can identify patient-specific variables and personalized therapy. The applications of modeling and simulation approaches save huge time and resources in discovering and developing drug treatments [33]. As a result, scientists created powerful computer algorithms that can take these theories into account. Such software is used to forecast the pharmacokinetics of oral drugs and make medication candidate selection easier and assist with regulatory policy implementation. Examples are MATLAB, Stella, NONMEM, AcsLx, Phoenix, and Berkeley Madonna, R, Simbiology, Mathematica, Monolix, WinNonlin, ADAPT, NAPP, CMATRIX, PKQuest, MCSIM, and BioDMET, Simcyp Simulator, GastroPlus, and PKSim.

Reducing the expense, duration, and number of in vivo studies required for the discovery, development and approval of generic medicinal products globally is a goal shared by the pharmaceutical industry and regulators. Therefore, in silico, PBPK modelings are becoming more widely acknowledged by pharmaceutical industrial companies as they have adopted this approach in different stages of drug discovery and development. Moreover, the PBPK modeling and simulation are considered in regulatory agencies such as the United States Food and Drug Administration( US,FDA), the European Medicines Agency (EMA), and the Ministry of Health Labor and Welfare (MHLW) of Japan [34]. Additionally, FDA and EMA have released detailed guideline notes on conducting and reporting PBPK investigations (EMA, 2016; FDA, 2018). Therefore, the number of research studies incorporating PBPK modeling has risen dramatically in the last two decades, illustrating the broad use of this approach in the scientific community [35].

The use of saliva sampling in TDM to describe the time course in combination with PBPK modeling can constitute a unique strategy with broad applicability for assessing drug exposures [36]. This should be done considering the drug characteristics as plasma and saliva PK are expected to be comparable for some drugs [18] [37]. Utilizing simulations of bioequivalence studies can further aid in predicting drug exposure from saliva [38]. Published papers have illustrated examples of utilization and verification of this strategy using atomoxetine [39], gentamycin [40], lead [41] [42], mycophenolic acid [43], Linagliptin [44]. The computational models of the PBPK approach can extend in predicting and better understanding the mechanistic change of drug response in different populations or disease cases based on saliva samplings [39] [40]. Studies showed that sing saliva samples are a highly successful strategy for pharmacogenetics and pharmacokinetics studies [45]. Therefore, incorporating saliva samplings with PBPBK modelling may have potential in personalized medicine studies, which can optimize treatment and reduce the cost, time and resources.

Conclusion

Therapeutic drug concentration monitoring is a significant practice to adjust and maintain safety and efficacy. Using salivary-based sampling in TDM has many advantages. Linking the PBPK modelings, given their highly beneficial features, to the salivary TDM can further aid in predicting and optimizing in vivo drug response. This review discusses saliva-based sampling in TDM and shows how PBPK can maximize the benefits of using saliva samplings in drug monitoring.

Conflict of interest

The author declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Publication History

Received: 10 September 2022

Accepted: 03 October 2022

Article published online:
11 November 2022