**4. Prediction of Human Pharmacokinetic-Pharmacodynamic Relationship**

In order to translate drug effects from preclinical information to the clinical phase of drug development, defining a drug exposure-response relationship using preclinical information is of importance. The PK-PD relationship is quantitative, predictive, and reproducible and is valid in all disease models [57,58]. Thus, characterizing this relationship is of great benefit in preclinical PK-PD studies to help guide dose selection and study design in humans. Exhaustive reviews of preclinical experimental methods that quantify exposure-response relationships have previously been performed [97,98]. These methods, such as classical time-kill experiments, hollow-fiber system (HF), different murine models, rabbits and guinea pig, all mimic elements of the human pathology to a certain extent, but all have their limitations. Here, the focus is on their informativeness of the exposure-response relationship for translation to human prediction.

*In vitro* determination of the minimum inhibitory concentration (MIC) is informative about the sensitivity of the bacterial strain to the compound. This is especially the case when the target sizes of the *M. tuberculosis* infection, macrophages, are utilized as environmental context [97]. The MIC is a measure of the net effect of the drug on bacterial growth and survival. However, it is very crude and undynamic as it is measured at a specific concentration and after a fixed time, which might cause it to deviate from the true MIC [99]. The MIC is also limited because the resolution is determined by the chosen dilution steps, and bigger dilution steps increase the risk of under- or overestimating the MIC. In addition, the determination of MIC is based on visual inspection which makes it prone to subjective error [99]. Mouton et al. have studied the variability between MIC measurements in *Staphylococcus aureus* treated with linezolid and have concluded that over half of the variability in the MIC measurements is either due to systemic and significant inter-laboratory differences or differences between strains [100]. The other half can be explained by assay variation and different environmental conditions, such as the media used and incubation temperature [100].

Several preclinical animal models for TB are in use. The advantage of an animal model over *in vitro* systems is the holistic environment of a whole organism, including a functioning immune system, physiological feedback systems and (drug) disposition. This results in more variability in the determination of the exposure-response relationship and requires more effort to elucidate drug effect from, for example, the immune system. The most emphasis is placed on murine models of TB [101], although there are arguments that the mouse is not a good model for TB in humans [102]. Mice can be housed in the required biosafety laboratories with ease, blood and tissue sampling is well established, and both chronic and latent infections have been successfully used [103]. However, the mice have a low susceptibility to *M. tuberculosis* and show only loosely organized granulomas, and are therefore limited when considering lesion-specific treatment. Granuloma formation in guinea pigs and rabbits is more representative of human granulomas, including caseous necrosis [102]. Guinea pigs are highly susceptible to *M. tuberculosis* which makes infection as straightforward as exposure to exhaustion from TB patients [103]. Rabbits are also utilized to study a slower response to treatment, disease relapse, and resistance development due to lung cavities, and their size makes studying drug distribution to TB lesions more feasible [98]. The experimental toolbox regarding immunologic reagents and genetic techniques is, however, more restricted in these animals, and both need more difficult and expensive husbandry. Granuloma formation can also be studied non-invasively in the zebrafish, a relatively new disease model organism in drug discovery and development [103]. Because of their transparency and easy genetic modification, fluorescence microscopy of pathogen and immune cells can be leveraged to follow infection and treatment [104]. With the small size and high fecundity of the zebrafish, high-throughput assays are available to test large numbers of compounds in short amounts of time with enough statistical power [105]. Methods to quantify internal drug exposure have also been established [106–108]. Recently, an exposure-response relationship has been developed for isoniazid in the zebrafish, which translated well to humans [109]. In general, non-invasive imaging of lesion pathology by computed tomography (CT) and positron emission tomography (PET) has the potential to improve the comparison between preclinical and clinical measurements of disease progression and treatment [102]. Ordonez et al have demonstrated this by using dynamic [11C]rifampicin PET-CT imaging in patients newly diagnosed with pulmonary TB and rabbits infected with cavitary TB to noninvasively measure intralesional drug concentration-time profiles and, consequently, time to bacterial extinction [110]. They also employed integrated modeling of the PET-captured concentration-time profiles in hollow-fiber bacterial kill curve experiments to predict the rifampicin dose required to achieve a cure in 4 months, which has a huge potential in antimicrobial drug development to shorten TB treatments [110]. It is clear that no single animal model represents a heterogeneous disease such as TB. A mechanistic understanding of TB in humans will identify which elements are characterized best by which animal model [103]. Independently of which preclinical experimental method is utilized, the sampling design of both PK (e.g., drug and/or metabolite concentration) and PD (e.g., infection, bacterial burden) biomarkers is of the utmost importance. The careful selection of datapoints over the duration of the experiment and at different drug concentrations is essential for a reliable quantification of the exposure-response relationship.

Regulatory agencies suggest determining PK/PD indices based on preclinical data for antibiotics, e.g., the area under the concentration curve over MIC (AUC/MIC), the maximum concentration (Cmax) over MIC (Cmax/MIC), and the percent of a 24-hour time period that the drug concentration is above MIC (T > MIC), for the establishment of the PK-PD profile of antimicrobials and for deciding the most optimal dosing regimens. PK/PD indices are based on preclinical studies that describe the PK-PD relationships of antimicrobials [111]. However, PK/PD indices suffer from several clear limitations, some of which are inherent to their use of MIC, the limitations of which are discussed above. Using PK/PD indices ignores information about the time-course of individual PK and PD processes [112]. As summary endpoints, they lack the ability to track the changes in the bacterial load over time [113]. Furthermore, when using AUC/MIC as a PK/PD index, the rate of drug administration is ignored, while, when using Cmax/MIC, bacterial killing is assumed to depend solely on the maximum drug concentration, ignoring drug half-life and infusion duration [99]. Using T > MIC assumes that the maximal drug effect has been reached when MIC is reached, regardless of whether higher concentrations were given [99]. Additionally, the colony-forming units (CFU) versus PK/PD indices profile shows great variability in the CFU observations for the same PK/PD indices value [99]. These PK/PD indices are selected and predicted as PD targets using HFS-TB to quantify a more realistic *in vitro* exposure-response

relationship that is translatable to *in vivo* [98,99,114]. However, despite EMA's qualification of the preclinical HFS-TB to be used to complement existing methodologies, it still suffers from a number of limitations. The EMA advises caution when interpreting HFS-TB results, as many instances of overand under-estimates of the drug's anti-TB activity have been reported [115]. In addition, HFS-TB cannot replace animal models or clinical studies [116], while the reproducibility of the method by other laboratories has not yet been assessed [115].

Mechanistic, or semi-mechanistic, PK-PD models in TB based on preclinical data allow for the description of the multiple mycobacterial populations present. A mechanism-based PK-PD model by Hollow-fiber systems for TB has the advantage of being able to mimic dynamic PK in comparison to more traditional static time-kill experiments. A semi-mechanistic PK-PD model can be derived using HTS data [117–119]. Khan et al. describes susceptible, resting, and non-colony-forming bacterial populations [120]. The multistate tuberculosis pharmacometric (MTP) model is a semi-mechanistic mathematical model that can describe and identify the exposure-response profile of a drug towards three bacterial subpopulations: fast-, slow-, and non-multiplying bacteria. It has been successfully applied to describe *in vitro* [121], mouse [122], and clinical data [123]. In addition, the MTP model has been successfully used in an MID3 approach, to predict observations from early clinical studies using clinical dose-response forecasting from preclinical *in vitro* studies of rifampicin and in combination with isoniazid [15,16]. This model has been selected by *The Impact and Influence Initiative* of the Quantitative Pharmacology (QP) Network of the American society of Clinical Pharmacology and Therapeutics (ASCPT) to highlight the most impactful examples of QP applications where the role of quantitative translational pharmacology has bridged science and practice to make better, faster, and more efficient decisions in drug discovery and development [25]. Another mechanism-based model is the Magombedze et al. model that mimics the disease state in TB patients by describing the mycobacterial population as logarithmic growth-phase, semi-dormant, and persister bacilli [117]. In addition, a pulmonary PK-PD model of isoniazid has been developed to better characterize the relationship between its PK and its anti-TB effects in the lungs [124].

#### **5. Prediction of Human Drug-Drug Interactions**

Tuberculosis requires a combination therapy of three different antibiotics or more, which increases the risk of DDIs. Drug-drug interactions between drugs that are intended to be used in combination should be considered as early as possible. The prediction of DDIs from preclinical data will improve the ability to predict the total efficacy of the combination in relation to the drugs in monotherapy, as well as compared to expected additivity, i.e., the sum of all effects from the drugs when given alone. DDIs that result in less efficacy in the combination than in a combination with one less drug should be avoided. However, combinations that result in an efficacy less than the expected additivity, but still result in more efficacy than when one drug is omitted, can be considered. Drug-drug interactions can relate to both PK interactions, i.e., one drug (the perpetrator) impacting the absorption, distribution, metabolism, or excretion of another drug (the victim), or PD interactions, i.e., the perpetrator impacting the potency or efficacy of the victim drug.

Regulatory guidelines on the investigation of DDIs are brief about the use of*in vitro* data, while in an MID3 context, knowledge on the relevant mechanisms of, e.g., metabolism combined with *in vitro* data can be leveraged to decide on suitable combinations of drugs without extensive experimentation [125]. Both *in vitro* studies as well as animal experiments can be utilized to assess the potential for PK DDIs [126]. *In vitro* studies make use of metabolically active hepatocytes or cells overexpressing drug transporters to determine the PK interaction potential of a new drug [127]. When studying DDIs in preclinical species, the between-species differences in transporters or enzymes should be taken into account [128]. Pharmacokinetic DDIs mostly impact drug clearance by the induction or inhibition of metabolic enzymes like those from the CYP family and, to some extent, ABC and transport proteins. Such an interaction by the perpetrator drug will greatly enhance or reduce the exposure of the victim drug. For example, rifampicin induces bedaquiline clearance 5-fold, and should therefore

not be combined for therapy [129]. Because bedaquiline has a very long terminal half-life, potential DDIs are difficult to identify using traditional methods, whereas properly designed experiments and quantitative modeling are necessary to elucidate such interactions [130]. Drug distribution can also be impacted because of the induction or inhibition of drug transporters like the permeability glycoprotein (P-gp), which is present on the canalicular membrane and blood-brain barrier, among others. Physiology based pharmacokinetic modeling can be very successful to predict metabolic DDIs, and specific DDI studies can be assisted by modeling and simulations [131]. Some anti-TB drugs are reported to be substrates for different hepatic enzymes or known to be inducers or inhibitors of metabolic enzymes. Rifampicin is well known as a CYP3A4 modulator [132,133], as well as an inducer of P-gp [134]. Additionally, even though the effect of clofazimine on CYP3A4 and P-gp is still unclear, clofazimine has been shown to delay the time taken to reach Cmax of rifampicin [135]. Horita et al. studied the effects of anti-TB and antiretroviral drugs on CYP3A4 and P-gp, and they found that clofazimine exhibits weak inductive effects on CYP3A4 [136]. Furthermore, the co-administration of bedaquiline and clofazimine has been reported to increase the risk of QT prolongation [137,138]. As described above, these potential DDIs can be predicted from *in vitro* data through, for example, *in vitro*-*in vivo* scaling [139] or PBPK [140]. A transcription/translation model and a PBPK model have been developed to predict rifampicin-induced DDIs with reasonable accuracy [141].

In contrast to PK interactions, due to clearly defined processes of absorption, distribution, metabolism, and excretion, PD interactions are harder to investigate and quantify. This is because, since a clinical DDI study has to study the drugs both alone and in combination, the number of arms in the study will substantially increase when studying three or more interacting drugs. The Greco model [142], which is derived from Loewe additivity, was developed to assess PD interactions. However, such a model suffers from being limited to interactions between only two drugs. On the other hand, the general pharmacodynamic interaction (GPDI) model overcomes this limitation, in addition to being flexible to different drug interaction data without requiring knowledge on the modes of action of the studied drugs [143]. The GPDI model-based approach proposes a PD interaction to be quantifiable, as multidirectional shifts in drug efficacy (Emax) or potency (EC50) and explicates the drugs' role as victim, perpetrator or even both at the same time. The GPDI model has been utilized along with the MTP model [121] to develop a model-informed preclinical approach for the prediction of PD interactions [144]. The MTP-GPDI model has been further employed to successfully evaluate and quantify the PD interactions of anti-TB drug combinations in mice [145]. Furthermore, it has been demonstrated that the GPDI model outperforms conventional methods in the evaluation of PD interactions for TB drugs [146].

It is clear that the need for a combination therapy of TB could potentially result in DDIs in the clinic. It is therefore essential to quantitatively understand the DDIs, both PK- and PD-interactions, as early as possible in drug development. Utilizing data from *in vitro* combination experiments combined with preclinical *in vivo* data on the exposure-response relationships of the drugs in combination and early clinical data, will inform on which combinations of drugs at which doses are efficacious and safe for patients. This quantitative integration of data and translation to the clinic is possible through the MID3 model-informed framework.

#### **6. Conclusions**

The development of new combinations of anti-TB drugs is both promising and challenging. Novel drug combinations and drug delivery routes require novel and innovative techniques. Model-informed drug discovery and development is an integrated framework of preclinical and clinical data through translational models that show great promise in selecting and predicting which drug regimens to carry forward to be evaluated in clinical trials. The MID3 framework supports decision making in drug development in relation to the prediction of efficacious and safe combinations of new drugs and translates this to the clinic. It is essential for drug developers to collaborate across

disciplines, and academic and industry borders and train a new type of scientist in experimental and computational innovation.

**Author Contributions:** All authors contributed to conceptualization of this work. All authors contributed to original draft preparation, review and editing. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.
