**1. Introduction**

Drug discovery research and development has experienced two periods with different centric strategies, namely phenotypic-based drug discovery (PDD) and target-based drug discovery (TDD) (Figure 1). Commonly PDD refers to an approach without prior knowledge of the target. In phenotypic-based screening, compounds that modify a phenotype to generate a positive outcome in cell culture or in a whole organism are identified. TDD examines a specific drug target which is hypothesized to play an important role in disease.

**Figure 1.** The evolution of technologies and screening strategies in drug discovery from 1910 to 2020. Adapted from [1,2].

Before the 1980s, the era without recombinant DNA technology, PDD was the primary approach in drug discovery. Most drugs at that time were discovered serendipitously by phenotypic assays in live animals or isolated tissues [3]. Examples are penicillin isolated from a *Penicillium* species in 1928 [2,4] and ivermectin isolated from *Streptomyces avermitilis* in 1975 [5]. From the 1990s, the development of genomics allowed the identification of drug target proteins. The advent of high-throughput screening and combinatorial libraries enabled the screening of target proteins in high throughput. Due to the development of technologies including X-ray crystallography, computational modeling and screening (virtual docking), visualization of the interaction of target protein and compound greatly facilitated the later stage of structure-based development. The development of these technologies appealed to the pharmaceutical industry and academic researchers who then switched to focus on TDD during the last three decades.

In the era that mainly focused on TDD, the total number of new molecular entities (NMEs) and new biologics approved by the Food and Drug Administration was far below expectations [6]. The timeline of cumulative NME approvals from 1950 to 2008 was contributed to by the three most productive companies in the industry and showed almost straight lines, indicating that productivity continued at a constant rate for almost 60 years [1]. The introduction of new molecular biology tools such as recombinant DNA technology, deep sequencing, mining of Expressed Sequence Tagged (cDNA) libraries and the draft human genome did not facilitate drug innovation as expected. This was also indicated in a later NMEs analysis with a timeline spanning over five years to 2013 [7]. More dishearteningly, while the number of NMEs per year has remained relatively constant for the past four decades, the investment in pharmaceutical research and development (R&D) has increased dramatically to over 50 billion USD per year. Today the number of NMEs launched per billion dollars of investment is well below the return for an equivalent billion-dollar investment 50 years ago [1]. The asymmetrical output raises questions about the limitation of the popular target centric strategy of R&D in recent decades.

Consequently, there is a revival of interest in PDD. A significant analysis by Swinney demonstrated that the majority of NMEs approved by the FDA during the 10-year period between 1999 and 2008 were discovered using phenotypic assays, where 28 came from phenotypic screening approaches and 17 came from target-based approaches [8]. It was suggested that the lack of successful NMEs in the post-genomic era was mainly due to the limited use of phenotypic screening. Because an organism is a complex biological system, a simplified single protein assay may not e fficiently represent the disease pathogenesis [8] The e ffects on a single protein may not be translated to meaningful therapeutic e fficacy. Conversely, phenotypic screening is an approach for unbiased targets. Phenotypic-based screening is, therefore, being reconsidered for screening compounds due to the realization that results for a single target protein may not fully correlate in the context of a complex biological process [9]. Phenotypic screening also holds the unique promise to uncover new mechanisms of action for currently untreated diseases such as rare diseases and/or neglected diseases [2].

The current phenotypic based approach should not be regarded as a step back to the classical phenotypic screening but as a new discipline [7]. Currently, in vivo and in vitro approaches are involved in phenotypic screening, of which in vitro cell-based phenotypic assays can be easily adapted to a high-throughput format for automated phenotypic analysis. New technologies such as gene expression, genetic modifier screening, resistance mutation and computational inference are increasingly being applied in phenotypic screening. These sophisticated phenotypic screening methodologies enhance the identification of novel compounds as well as their mechanism of action [10].

Target identification is a crucial part of drug discovery. Most large pharmaceutical companies strongly recommend target identification because failing to assign the mechanism of action is frequently regarded as a major risk factor for clinical development and regulatory approval [8]. Even with the most advanced phenotypic screens, in most cases, it is still di fficult to determine the mechanism of action. Target identification is the key value of TDD [6]. With the target protein in hand, detailed drug-protein characterization is possible and this provides a better understanding of structure-activity relationships, which is a challenge in phenotypic screening without a known target [11].

Considering the strengths and weaknesses of PDD and TDD, these two approaches should not be treated monolithically but as complementary approaches that can work together to increase the productivity of drug discovery and development. This article describes an approach that combines PDD and TDD: PhenoTarget screening to identify active compounds from natural resources. As a proof of principle, we apply this combination approach to probe fractions of natural product libraries for compounds active against *Mycobacterium tuberculosis* H37Rv, the etiological agen<sup>t</sup> responsible for tuberculosis (TB). The active fraction was then screened against a panel of 37 unique mycobacterial proteins to identify the potential compound and protein responsible for the activity against *M. tuberculosis*.
