**1. Introduction**

Tuberculosis (TB) remains one of the top 10 causes of death worldwide, with 10 million new cases and 1.4 million deaths in 2018. The problem of antimicrobial drug resistance (AMR) is rising, with drug resistance associated with 3.4% of new TB cases globally and up to 50% of previously treated cases in some areas of the world [1]. The discovery of new drugs to treat *Mycobacterium tuberculosis* (*M.tb*) is challenging, with only pretomanid, delamanid and bedaquiline marketed for use in the last 40 years despite sustained international efforts [2]. Multiple logistical and physiological factors contribute to the difficulty of this task (reviewed eloquently elsewhere [3–5]). They include biosafety constraints of working with a slow-growing pathogenic bacterium, heterogeneity of clinical disease and bacterial phenotypes in vivo, intracellular and extracellular *M.tb* sites, drug penetration into lung pathology, the lipid-rich *M.tb* cell wall as a barrier to drug uptake and intrinsic drug resistance, limited number of validated drug targets, the requirement for combination drug therapy, and the length and cost of clinical trials. Omics technologies aim to measure and evaluate together the ensemble of molecular entities by biological class to understand the contribution of each component. Whatever the category of molecule under investigation, the key advantage of omics approaches is that they are unsupervised, and thus less biased by dogma, which is valuable for overcoming drug development bottlenecks [6,7]. Omics in a hypothesis-generating discovery biology setting, is an excellent means of identifying new targets for drug discovery. In a compound-first (discovery chemistry) approach, liberated from reductionist assays, omics technologies are useful tools to reveal or confirm drug mode of action. Mycobacterial omics are applicable throughout the drug development process from initial drug discovery to preclinical and

clinical stages, at each step describing the action of compounds, derivatives, and formulations on *M.tb*. For example, identifying target drift or off-target effects during lead optimization, or characterizing drug resistance conferring mutations in clinical trials.

This review summarizes the application of *M.tb* omics strategies in the early stages of the discovery of new drugs for TB, incorporating genomics (DNA), transcriptomics (mRNA), proteomics (proteins), metabolomics (metabolites) and lipidomics (lipids). The review is not intended to be comprehensive—omics are now fully established in most drug discovery settings—but aims to highlight landmark and interesting approaches to the TB drug development problem. The review centers on omics applied directly to *M.tb*, using examples from other mycobacterial models only to illustrate groundbreaking discovery tools. We focus on (a) target identification, in this context the recognition of potentially druggable pathways worthy of drug discovery efforts in a target-based approach; (b) mode of action studies, often aimed at progressing hits from whole cell compound screening strategies on the long road to the TB clinic (Figure 1).

**Figure 1.** The application of omics tools to antimicrobial drug discovery pathways. In a discovery biology approach, omics methodologies are used early in the process to identify new targets for investigation. In a discovery chemistry setting, omics techniques are useful further down the road to identify mechanism of action of lead compounds.
