**3. Prediction of Human Pharmacokinetics**

*In silico* ADME-PK (absorption, distribution, metabolism, excretion, and pharmacokinetics) is the use of computer modeling to understand structure−property relationships and to predict DMPK (drug metabolism and pharmacokinetics) properties from compound structure. This is related to but distinct from physiologically based pharmacokinetic (PBPK) modeling, which strives to provide accurate predictions of the PK profile of drug candidates [46]. The focus of *in silico* ADME-PK is to guide the design of novel compounds with superior ADME properties. Most often a quantitative structure−property relationship (QSPR) approach is used to relate a compound's structure to the chemical property in question (e.g., cell permeability or metabolic clearance) measured in an *in vitro* assay. Related terms are also quantitative-structure-activity relationships (QSAR), when a set of predictor variables is related to the potency of the drug.

Orally administered products are subject to a sequence of transport and enzymatic barriers in enterohepatic systems affecting bioavailability, including extraction in the intestinal and liver tissues, which could impact the fraction of the orally administered dose that reaches the systemic circulation and thereby the site of action. Bioavailability is mainly dependent on three general and rather complex serial processes: the fraction of the oral dose that is absorbed (Fabs), the first-pass extraction of the drug in the gut wall (EG), and the first-pass extraction of the drug in the liver (EH) [47]. In general, oral products with a low F (<25%–35%) have a higher inter- and intra-individual variability in plasma exposure (coefficient of variation >60%–120%) [48]. Drugs with high degree of Fabs show sufficiently high solubility of the active pharmaceutical ingredient (API), no luminal degradation, and absorption along the small and/or large intestine [49,50]. The regulatory framework Biopharmaceutics Classification System (BCS) of drugs provides information relevant to understanding and predicting GI drug absorption and bioavailability in general, which is relevant to the absorption potential in the small and large intestine [51]. After the drug is absorbed, it passes to the liver, which expresses a broader range of different enzymes compared to the intestine [52], such as the family of CYP enzymes [53]. Other enzymes such as microsomal uridine 5'-diphospho-glucuronosyltransferases (UGTs), sulfotransferases, and glutathione S-transferases are

found in great amounts in the human liver [54]. Humans show large inter-individual variation in the amount of different enzymes, which accounts, in part, for the large inter-individual variation reported for EH and CL. A considerable large inter-individual variation in the expression of the different CYP isoforms has also been observed, ranging from 20-(CYP2E1 and CYP3A4) to >1000-fold (CYP2D6). The liver is also the major organ for glucuronidation in the body. Glucuronide conjugates have molecular characteristics that are associated with biliary excretion of a compound, i.e., a high molecular weight, ionized and the presence of polar groups [55].

The identification and quantification of the important PK processes described above can be investigated in relevant *in vitro* models, and predictions of PK properties like regional intestinal permeability can be made early [56]. Most importantly, an estimate of human drug clearance will determine how fast a drug is eliminated, and conversely define the dose range to study in FIH [57]. Several different models have been suggested for the prediction of oral absorption for the biopharmaceutical design of oral drug delivery systems [58,59]. The proposed BDDCS has been shown to be useful in predicting some crucial ADME parameters and especially the transport/absorption/elimination interplay [60]. Preclinical data can then be translated through *in vitro*-*in vivo* extrapolation, or even through PBPK modeling to generate basic PK parameters such as fraction of dose absorbed, bioavailability, clearance (CL), volume of distribution, and terminal half-life. Furthermore, the accuracy of the QSAR predictions of effective intestinal permeability (Peff), is significantly improved when based on a combination of molecular physicochemical descriptors and molecular dynamics simulations from *in vitro* data [61]. Molecular simulations have been successfully used to predict the effects of cholesterol in the lipid membrane fluidity [62]. Additionally, molecular simulations have been reported to be useful as they are comparable to experimental data [63]. Among the molecular descriptors evaluated by Lipinski (e.g., polar surface area, hydrogen bond donors (HBDs)/acceptors, Log D), the number of HBDs is the most restrictive when it comes to intestinal membrane transport/absorption [64,65]. Two drugs violating this rule (i.e., >5 HBDs and low intestinal Peff and Fabs), one of which is rifampicin, have been investigated thoroughly and offered a potential explanation for drug absorption beyond the Lipinski Rule-of-five [66]. Based on a liposomal permeation assay, it has been proposed that drug molecules with more than five HBD can be sufficiently absorbed in the intestine by passive lipoidal diffusion [66]. Some drugs are absorbed by passive lipoidal diffusion despite their unfavorable physicochemical properties. It is therefore necessary to find more complex descriptions of the molecular interaction by applying a combination of experimental data and molecular dynamic modeling and simulation to further improve the accuracy in predicting general membrane transport across the cellular membrane barrier and not only in the GI-tract [66–69].

Physiologically based pharmacokinetic modeling is considered to assist drug product development by providing quantitative predictions through a systems approach [70]. A mechanism-based model, like that of the PBPK approach, separates drug-specific from system-specific elements, which allows for the interspecies translation of the time course of the drug [41]. Physiologically based pharmacokinetic models divide the body into anatomically and physiologically meaningful compartments, including the gastrointestinal tract for absorption, the eliminating organs, and non-eliminating tissue compartments [71]. In addition, compound-specific parameters such as physicochemical and biochemical parameters (e.g., tissue/blood partitioning and metabolic CL) are incorporated into the model to predict the plasma and tissue concentration versus the time profiles of a compound in an *in vivo* system following intravenous or oral administration. Translation between species, special populations, or disease states, are the result of changing these physiological parameters accordingly. Several variations are in use, including a whole-body PBPK model describing the complete organism, and hybrid PBPK models, combining PBPK elements with empirical compartmental PK to simplify the model [72]. An important element in the physiologically based translation of PK is binding of the drug of interest to proteins in the plasma, mainly albumin, lipo- or glycoproteins, or globulins. Protein binding differs between experimental settings (*in vitro*) and species (*in vivo*) and should be taken into account because it influences tissue penetration and the free drug that can interact with its

target [73]. Physiologically based pharmacokinetic modeling predictions are a valuable tool in the pharmaceutical industry due to the possibility of combining all the available and relevant information that is generated during the preclinical stage, which helps improve decision making during the selection process [74]. For instance, biorelevant dissolution-absorption PBPK modeling and simulation has been reported as applied in 88% of early drug development processes [75]. Moreover, PBPK modeling provides a powerful tool to study potential PK DDIs through incorporating the drug's physicochemical properties, PK properties, human physiological variables, and inter-individual variability estimates.

Predictions of PK parameters use information from preclinical studies in animals to transition to clinical trials, i.e., FIH studies, when no clinical information is available to guide the decision of the starting dose. Thus, the estimation of the starting dose in human subjects relies on the PK knowledge of the drug from different species. It is essential to have preclinical PK data based on blood, plasma, and/or tissue sampled longitudinally to optimally capture the complete drug profile during a dosing interval. A model-based approach to the starting dose often uses allometric scaling to predict human drug clearance and distribution volumes. Allometric scaling is based on the assumption that physiological similarities exist between different species arising from anatomical similarities, specifically similarities in body weights and body surface areas [76]. Historically, a maximum dose for FIH studies was based on the no observed adverse effect level (NOAEL) in preclinical experiments, an arbitrary safety factor, and allometric scaling. This approach is empirical by nature and therefore limited. When more mechanistic data and models are available, a minimal anticipated biological effect level (MABEL) can be estimated [42]. For example, preclinical PK-PD and interspecies differences in the target can be utilized to estimate the MABEL for a FIH trial [77]. This has the benefit of being driven by pharmacology, where FIH trials will answer pharmacological questions on PK and PD, rather than being driven by toxicology or tolerability. Taking into account MABEL and safety factors, a first study with single ascending doses (SAD study) will quantify the PK and ensure safety and tolerability. A second study with multiple administered doses (MAD) can subsequently be designed accordingly [78].

In addition to allometric scaling, the use of IVIVC has markedly increased [79–82]. It is suggested that animal PBPK models should be used as part of a stepwise approach, in which the first step uses animal data to understand the processes and verify the predictive power of *in vitro* systems, and the second step is about forecasting human PK from *in vitro* data and *in silico* methods (learn-and-confirm) [82]. The first step in predicting drug CL using IVIVC is to obtain intrinsic CL (CLint) from *in vitro* data [83,84]. *In vitro* CLint values determined from various systems including hepatocytes, cell transport models, liver or intestinal microsomes, or recombinant CYPs, either by substrate depletion or metabolite formation, are normalized for cell, microsomal protein or enzyme concentration. The next step consists of scaling the activity determined *in vitro* to the whole liver by the use of a scaling factor, to account for incomplete microsomal recovery from the tissue to obtain *in vivo* CLint. Finally, the third step involves the use of a liver model which incorporates the effects of hepatic blood flow, plasma protein-binding and blood cell partitioning to convert the estimated *in vivo* CLint into a hepatic CL (CLH). The well-stirred liver model is most commonly used, but the dispersion model or the parallel tube model is also available [84].

Using high doses of oral anti-TB drugs may result in high plasma concentrations, leading to an increased risk of adverse effects [85] while not ensuring adequate concentrations at the site of action [85,86]. This has prompted investigation into the use of the pulmonary route to deliver anti-TB drugs directly to the site of action in the lungs. Administering anti-TB drugs as inhaled formulations ensures the delivery of the drug directly to the target organ, avoiding any unwanted systemic side effects, thereby improving patient compliance [87]. Optimal pulmonary drug delivery for locally acting drugs includes a high local lung concentration, extended lung residence time and low systemic concentration [88]. A fundamental understanding of pulmonary dissolution, residence time, and lung absorption processes is key for the successful development of inhaled products [89,90]. However, inhaled formulations have many challenges, including formulation stability, pulmonary distribution, lung toxicity, and additives safety [91]. Furthermore, dosing of inhaled drugs is more complicated than other routes of administration as their absorption in the lung is highly variable [91]. Thus, many *in vitro* and *in vivo* models have been developed to study the PK of inhaled anti-TB drugs, specifically their absorption and distribution, in order to evaluate the efficacy and safety of anti-TB drugs in the lungs. These models, while allowing a reduction in biological complexity, still face many challenges, and can be demanding to build [92]. *In vivo* animal models are the gold standard regarding the assessment of drug clearance, systemic side effects and PK after pulmonary administration [92]. However, animal models are not always able to mimic human pulmonary anatomy and physiology, or TB disease progression in humans, and they do not exhibit extrapulmonary dissemination similar to humans [86]. Translation from animal data to the clinic has been recognized as challenging [93]. Understanding the pulmonary exposure is important, and animal data can contribute specific information about the lesion to plasma ratio [94], as similar lung distribution ratios can be obtained in human. However, little is known about the factors that influence drug distribution from plasma into the range of tissues, nodules and cavities that are inhabited by the TB pathogen. Pulmonary TB lesions consist of a diversity of cell types, tissue structures and vascular architectures which suggests that the distribution of the drug is not only governed by passive equilibration between unbound drug concentration in plasma and tissue [95]. MALDI mass spectrometry imaging (MALD-MSI) is a new technique to study the distribution of small molecules in the various compartments of pulmonary lesions [96]. Information from such studies not only provides knowledge of regional differences in drug exposure, but also confirms a high exposure in regions where a high density of persistent TB bacteria is found.
