**2. Model-Informed Drug Discovery and Development**

Model-informed drug discovery and development is given by a quantitative framework for prediction and extrapolation, aimed at improving the quality, efficiency and cost-effectiveness of decision making in drug development [17]. It can also be utilized in early drug discovery through target identification and validation, and in describing the PK-PD and toxicological properties of the candidate drug. In addition, it increases the efficiency of trials and reduces the cost through facilitating dose and sample size selection [17]. Because of the great potential of MID3, it has been received well and implemented by drug developers [18,25]. The EMA supports MID3 and has built competence to meet the increasing modeling and simulation work in the dossiers submitted to EMA through the implementation of the modeling and Simulation Working Group (MSWG). Further, the EMA stresses that, in order to benefit from the full potential of MID3, stand-alone applications of modeling and simulation, dissociated from clinical decisions with respect to the design and objectives of clinical trials, should be avoided [19]. This is also pointed out in the MID3 white paper [17], where the implementation process is described as very important, and where the modeling and simulation work should be clearly motivated in the analysis, with clear objectives that are relevant and understandable for the entire development team. To realize the full potential of MID3, it needs to be integrated into the development plan rather than being seen as an ad-hoc activity [23]. The FDA has implemented a new Model-Informed Drug Development Paired Meeting Pilot Program which refers to the application of a wide range of quantitative approaches in drug development to facilitate the decision-making process, such as dose optimization, supportive evidence for efficacy, clinical trial design, and informing policy [26]. Despite the recent efforts within academia, EFPIA and regulatory agencies, MID3 has not been utilized to its full potential within TB drug development, where the need is great due to the complex development of new drug regimens consisting of at least three drugs.

Model-informed drug discovery and development builds upon pharmacometrics, the discipline that applies mathematical and statistical methods to understand, quantify, translate, and predict PK and PD behavior, including uncertainty in that behavior [27,28]. Pharmacometric population PK and PK-PD modeling can quantify these processes to better predict the concentration-time and exposure-response relationships of anti-TB drugs as compared to non-modeling techniques, such as non-compartmental analyses (NCA) for PK or traditional statistical analysis of, for example, the relationship between dose and baseline-reduced response at the end of treatment [29] The advantage of pharmacometric modeling is that it takes the inter-individual and inter-occasion variabilities into account. Once a population model has been developed and evaluated, various simulation techniques can be used, e.g., Monte Carlo simulations where virtual patients are drawn from the earlier quantified variance of variability in the population. Pharmacokinetic models are usually nonlinear mixed-effects models with unique parameters for fixed effects and random effects. Pharmacodynamic models can consist of a statistical method suitable for the biomarker or endpoint where time-to-positivity and relapse would be described with a time-to-event model, while colony forming unit (CFU) is a continuous variable and, as such, can be described with similar nonlinear mixed effects modeling.

Model-informed drug discovery and development is likely most impactful in the translation from preclinical to clinical, where the understanding and extrapolation of the exposure-response from preclinical to clinical is crucial. Model-informed drug discovery and development is also very important in the early clinical phases of anti-TB drug development, specifically phase II EBA trials, as it is difficult to investigate all drug combinations and associated PD interactions in clinical trials. The majority of the knowledge about the potential PD interaction space needs to come from preclinical information. Additionally, MID3 can be used to design the next preclinical or clinical study in order to optimize the likelihood of collecting informative data. A crucial step in drug development is the prediction of FIH design and associated doses. Model-informed drug discovery and development strategies and methods can be used to scale preclinical information to humans to design the FIH trial. Pharmacometric techniques have been shown to reduce the sample size needed in comparison to traditional statistical methods [29–31], while MID3 has been reported to save significant costs through its impact on decision making [17]. Preclinical experiments should be designed to be able to quantify the exposure-response relationship, including quantitative biomarkers relative to the interspecies' translation thereof [32]. An MID3 framework integrates all relevant preclinical and clinical information, and can therefore be used to back-translate results from the clinic to improve the preclinical understanding of the pathophysiology and pharmacology [33]. Even failed translations to humans are valuable in correcting the preclinical methods used. An iterative forward- and reverse-translational cycle has the potential to continuously enhance confidence in preclinical models [34]. The availability of large clinical datasets from, for example, electronic medical records accelerates reverse translation and improves the preclinical modeling of clinical manifestations [35]. Additionally, data from veterinary medicine can be utilized to guide human medicine development [36]. For this framework to really have an impact, data repositories and common languages are essential for application across different disciplines, disease areas, or stages of development [23,37]. In addition, to ensure that modeling and

simulation adds value through an MID3 approach, pharmacometricians must communicate with their project teams before any data analysis starts to understand the key strategic development questions, clinical context, available data, assumptions, and decision criteria [23].

The prediction of efficacy and safety in new drug combinations with new or unknown mechanisms of action will benefit from the next paradigm in drug development and MID3, namely quantitative systems pharmacology (QSP). This is the pharmacological perspective on a systems' modeling, a body-system-wide characterization of the health and disease of an organism based on a mechanistic and molecular understanding of the individual components in the context of the holistic network [38]. QSP is the middle-out interface between systems biology and pharmacometrics, describing the pharmacological perturbation within the studied context [39]. It accounts for differences in (molecular) mechanisms of a disease [40], which is very relative for TB with its heterogeneous pathophysiology of acute, chronic, and latent infections. Because of its mathematical description of all the relevant elements of the pharmacological and pathophysiological pathways, and their differences between species, it becomes key to translational medicine [41]. Because of this quantitative understanding of the network, the prediction of the effects of drugs with new mechanisms of action improves significantly [42]. The development of QSP models in the preclinical phase is, however, uncommon, and the dedicated acquisition of experimental data like transcriptomics or metabolomics for the development of QSP models is rare [43,44]. Quantitative systems pharmacology models are intended to be applied to a wider scale than the individual questions or problems they were originally developed for [45]. For TB specifically, this could mean a systems model of the *M. tuberculosis* infection in the human context of macrophage infiltration, granuloma formation and pulmonary lesion development, with all relevant pathways and drug targets quantitatively described. The effect of new combinations, including drugs with novel mechanism of actions, can be predicted.
