**1. Introduction**

Drug discovery and development is a challenging, lengthy, and costly process. The costs of a novel drug reaching the market can be as much as 2–3 billion dollars [1]. In the early discovery phase, libraries consisting of thousands of compounds can be synthesized chemically and tested for efficacy *in vitro* at a relatively low cost. The largest expenditures are in the late preclinical and clinical phases of drug development, where the efficacy and safety of treatment are assessed. Smart decisions need to be made regarding which compounds and regimens should progress through the preclinical phase and subsequently into clinical trials. Early characterization of each compound's exposure-response

relationship, i.e., pharmacokinetic (PK)-pharmacodynamic (PD) relationship and potential interactions within regimens and with commonly co-administered drugs, can allow for informative decision making throughout preclinical development and into clinical development [2].

Tuberculosis (TB) is the leading cause of adult mortality through infectious diseases and 10 million new cases are reported globally every year [3]. Sensitive TB is currently treated with a six-month regimen of antibiotics, consisting of isoniazid, pyrazinamide, rifampicin and ethambutol, which was developed in the mid-twentieth century. This therapy is believed to be suboptimal and was not developed using modern approaches for drug development, thereby lacking important information on the PK-PD relationship. Therefore, clinical trials have recently been conducted in order to define the relationship between exposure and efficacy, as well as safety, where statistically significant exposure-response relationships for rifampicin have been identified, in order to support a higher dose of rifampicin [4–7]. Almost one in five patients will acquire multidrug-resistant tuberculosis (MDR-TB) or rifampicin-resistant tuberculosis (RR-TB) [3]. Recently, the new anti-TB drugs bedaquiline, delamanid and pretomanid were conditionally approved againstMDR-TB, which led to updates to theWorld Health Organization (WHO) treatment guideline for MDR-TB [8]. Bedaquiline is a diarylquinoline, a new class of antibiotics. It is an inhibitor of the membrane-bound adenosine triphosphate (ATP)-synthase enzyme, therefore blocking mycobacterial ATP formation and energy metabolism. Bedaquiline is therefore bactericidal for dormant mycobacteria as well, a preferable feature for the shortening of treatment duration and prevention of relapse [9]. Delamanid is a nitroimidazole and affects the mycobacterial cell wall, thereby also improving drug penetration into the mycobacterium. It is the most potent TB drug and is active against replicating and dormant mycobacteria as well [9]. The combination of delamanid with bedaquiline is, however, not recommended, due to QT-prolongation-related cardiotoxicity [10]. Pretomanid belongs to the same class of antibiotics as delamanid [9]. Pretomanid was developed as part of a drug combination together with bedaquiline and linezolid, an oxazolidinone-class otherwise used for the treatment of pneumonia and skin infection. There is a clear need for the additional development of new effective drug combinations. The European Medical Agency (EMA) drug development guideline for TB specifies that efforts should be made to develop entirely new regimens to treat TB, rather than focusing on single drugs [11]. Due to the burden of polypharmacy for the patients and the increased risk of side effects, the focus should be on developing new regimens instead of the development of single agents as an add-on to a current regimen which was recommended in the earlier EMA TB drug development guideline [12]. Of the three new drugs against TB, only pretomanid is approved as a new combination regimen, while bedaquiline and delamanid were developed as add-ons to existing therapy [13]. The development of new combination regimens is the way forward, the acceleration of which is the objective of the new Innovative Medicines Initiative (IMI)-funded consortium European Regimen Accelerator for Tuberculosis (ERA4TB). It is important to assess drug-drug interactions (DDI), with respect to both PK and PD, to understand how the different drugs behave in certain combinations and doses in order to maximize the efficacy and potentially learn how the efficacy of the combination varies with time and concentration. The development of drug combinations is, however, challenging. It is difficult to demonstrate the contribution of an individual drug to a regimen regarding efficacy or safety [14]. The duration of treatment is lengthy, especially when considering follow-up to ensure no relapse. Moreover, the design and execution of preclinical experiments and clinical trials are complex, as the number of treatments to test grows exponentially with every added drug or dose, leading to longer development times and higher costs. Tuberculosis drug development, which focuses on regimens rather than unique drugs as an add-on treatment, thus challenges our methods to assess and identify optimal regimens. Therefore, smart experimental designs and optimized data analysis are essential. Data from larger scale *in vitro* preclinical experiments, with different drug regimens that explore the PD interaction space in order to investigate the synergism and/or antagonism of the interacting drugs, should be used to select the best regimens to determine the exposure range *in vivo*. Based on the exposure-response relationship in animals, and/or pure *in vitro* predictions, the first in-human (FIH) and early bactericidal activity (EBA) trials can be designed. These steps all require

a mathematical translational approach, taking into account the PK-PD and translational factors to account for differences between preclinical species and patients [15,16].

The European Medicines Agency/European Federation of Pharmaceutical Industries and Associations (EFPIA) Modeling and Simulation joint workshop held in 2011 assembled scientists from the pharmaceutical industry, academia and regulatory authorities from across Europe, the USA and Japan to consider the future role of modeling and simulation in drug development and regulatory assessment. As a follow up to the workshop, one of the EFPIA groups' commitment to EMA was to generate a "good practice" manuscript covering aspects of planning, conduct and documentation of a variety of quantitative approaches for modeling and simulation methods where the concept of Model-Informed Drug Discovery and Development (MID3) was defined [17]. The aim of MID3 is to enable more efficient and robust research and development and regulatory decisions using an integrated model-based drug development approach [17,18]. The MID3 strategy for the development of drugs in any therapeutic area is supported by the EMA [19]. The MID3 framework has been defined as a "quantitative framework for prediction and extrapolation, centered on knowledge and inference generated from integrated models of compound, mechanism and disease level data and aimed at improving the quality, efficiency and cost effectiveness of decision making" [17]. The MID3 framework should be applied in the development of new TB drug regimens and is necessary for the reliable prediction of the optimal selection of novel TB drug combination therapies based on pre-clinical information, and subsequent decisions on which combinations to evaluate in clinical trials in order to confirm their efficacy and safety. The framework integrates all available data and information on the disease and the compounds. In addition to PK and PD models, systems biology or systems pharmacology models [18] and machine learning based on, for example, imaging data [20] or even artificial intelligence (AI) [21,22] are important tools. Figure 1 shows the proposed MID3 strategy for the rapid development of anti-TB regimens through the prediction of human-concentration-time relationships (PK), exposure-response relationships (PK-PD) and DDIs to select FIH doses, as well as the prediction of Phase II and Phase III drug regimens. Initially in a drug development program, preclinical data is mostly available. The impact of modeling and simulation increases towards the prediction of human exposure-response. With this input efficient decision can be made about the optimal combination of different drugs, and the right dose for each drug in the combination. Currently, limited modeling and simulation are required for market approval, which relies more on statistical comparison between treatment groups after phase III [23]. However, modeling and simulation can have a role in the analysis of Phase III data in order to define the relationship between exposure and clinical endpoint, evaluate PK DDI and simulate alternative potential regimens in certain subgroups, for example, patients with renal impairment [24]. A key step for successful TB drug development is to use modeling and simulation to predict the efficacy of combinations, including DDIs, for, for example, synergy. We will review the necessary steps from this perspective for the successful MID3 application to the preclinical to clinical translation of efficacious TB drug combinations, regarding the optimal doses of drugs in complex regimens.

**Figure 1.** Illustration of the role of Model-Informed Drug Discovery and Development (MID3) and the application across preclinical to clinical drug development.

MID3, with modeling and simulation as key tools, is suggested to be applied throughout the pre-clinical to clinical drug development phases in order to optimize and inform decision making with respect to clinical trial design and the selection of drugs and doses to be carried forward from the preclinical phase and into clinical trial programs. The prediction of human-concentration-time relationships (PK), exposure-response relationships (PK-PD) in monotherapy and combination therapy, as well as drug-drug interactions, (DDI), requires the application of MID3 techniques and integration of all available data. In early preclinical drug development, preclinical data is used for predictions using, for example, *in vitro*-*in vivo* correlation (IVIVC), physiology-based pharmacokinetics (PBPK) and a biopharmaceutics drug disposition classification system (BDDCS) in order to define absorption, distribution, metabolism, and excretion (ADME) properties. Further down the developmental process, MID3 becomes more important in order to define exposure-response relationships and pharmacodynamic (PD) interactions using preclinical data for optimal design of first-in-human (FIH) and early bactericidal activity (EBA) trials. The need to define the optimal combination regimen using preclinical information data is evident, as the necessary number of clinical trial arms/experimental groups grow exponentially with the number of drugs within a regimen. Techniques using optimal design and simulation studies are essential and part of the MID3 framework. Throughout the process, the precision of human predictions increases. Different important drug development decision steps (circles) are subject to learn-and-confirm cycles, for example, early EBA clinical studies where the earlier defined exposure-response relationship using pre-clinical data (learning phase) is confirmed (confirming phase).
