Jean-Yves Trosset

The dynamics of the cell signaling network is highly regulated to adapt to temporal fluctuations and spatial heterogeneity of the microenvironment. Formal modeling of biological regulatory networks is an approach to identify which key components of this biological network, sometimes individual therapeutic targets, have a long-term influence on a disease-related phenotype of interest. We present an in silico formal screening strategy in the context of cancer metabolism to identify key hot spots in the metabolic network that could induce a systemic change of pathological cell phenotype such as the reversal of the Warburg effect.

Linking a drug’s mechanism of action to a disease-related phenotype is the greatest challenge in pharmaceutical research. Target-based and phenotype-based screening are the two basic tools to identifying drug candidates which display efficiency in triggering disease-related phenotype through a known Mechanism of Action (MoA) via a predefined primary target. In this paper, we present a reverse engineering approach which identify drug candidates from active compounds in phenotypic-based assay while bypassing the biochemical assay on the primary target. This important information is deduced implicitly by taking a reference drug with known MoA, i.e., a known primary target as a template. An in silico protocol is developed to preferentially select from phenotypic-active compounds, those that share the same target profile (primary and secondary target) as the reference drug. This is realized by requiring the same pharmacophore pattern as the reference drug while staying within its molecular envelop. These two constraints ensure a similar action on the primary target, while limiting the risk of digression with respect to the profile of secondary targets of the reference drug. Target-based screening assay can be used later to validate this in silico-based drug candidate selection. This reverse engineering approach can be used to crosslink two therapeutic indications if the reference drug has itself been validated experimentally on both. In that case, we use compound activity data from a phenotypic-based high-throughput screening campaign carried out on the target indication, in our case: malaria. The primary target is chosen indirectly through a reference drug and validated in the first indication (here cancer). If we choose a reference drug which is also validated in the malaria field, this implies that the heterologous version of the primary target in cancer field is also a key player of parasite proliferation within humans. We are using BIX-01294, an inhibitor of human histone lysine methyltransferase (HKMT), as a reference drug against cancer which has been validated experimentally to prevent plasmodium proliferation with human red blood cells through the inhibition of HKMT. The developed in silico protocol extracts general cytotoxic compounds with innovative chemical classes, yet with similar MoA as BIX-01294, i.e., inhibiting the original human HKMT, the biochemical bioassay on the plasmodium version of HKMT being not available in a kit-format. This reverse engineering approach is well adapted to transfer the knowledge associated with drug-targets in one therapeutic area (e.g., cancer) to another therapeutic indication for which the target-based approach is way more difficult. The reference drug plays therefore the role of a chemical probe to investigate targets in this target therapeutic area.

Adaptation of metabolism is a response of many eukaryotic cells to nutrient heterogeneity in the cell microenvironment. One of these adaptations is the shift from respiratory to fermentative metabolism, also called the Warburg/Crabtree effect. It is a response to a very high nutrient increase in the cell microenvironment, even in the presence of oxygen. Understanding whether this metabolic transition can result from basic regulation signals between components of the central carbon metabolism are the the core question of this work. We use an extension of the René Thomas modeling framework for representing the regulations between the main catabolic and anabolic pathways of eukaryotic cells, and formal methods for confronting models with known biological properties in different microenvironments. The formal model of the regulation of eukaryote metabolism defined and validated here reveals the conditions under which this metabolic phenotype switch occurs. It clearly proves that currently known regulating signals within the main components of central carbon metabolism can be sufficient to bring out the Warburg/Crabtree effect. Moreover, this model offers a general perspective of the regulation of the central carbon metabolism that can be used to study other biological questions.

This chapter will focus on today’s in silico direct and indirect approaches to assess therapeutic target druggability. The direct approach tries to infer from the 3D structure the capacity of the target protein to bind small molecule in order to modulate its biological function. Algorithms to recognize and characterize the quality of the ligand interaction sites whether within buried protein cavities or within large protein-protein interface will be reviewed in the first part of the paper. In the case a ligand-binding site is already identified, indirect aspects of target druggability can be assessed. These indirect approaches focus first on target promiscuity and the potential difficulties in developing specific drugs. It is based on large-scale comparison of protein-binding sites. The second aspect concerns the capacity of the target to induce resistant pathway once it is inhibited or activated by a drug. The emergence of drug-resistant pathways can be assessed through systemic analysis of biological networks implementing metabolism and/or cell regulation signaling.

Pharmacological science is trying to establish the link between chemicals, targets, and disease-related phenotypes. A plethora of chemical proteomics and structural data have been generated, thanks to the target-based approach that has dominated drug discovery at the turn of the century. There is an invaluable source of information for in silico target profiling. Prediction is based on the principle of chemical similarity (similar drugs bind similar targets) or on first principles from the biophysics of molecular interactions. In the first case, compound comparison is made through ligand-based chemical similarity search or through classifier-based machine learning approach. The 3D techniques are based on 3D structural descriptors or energy-based scoring scheme to infer a binding affinity of a compound with its putative target. More recently, a new approach based on compound set metric has been proposed in which a query compound is compared with a whole of compounds associated with a target or a family of targets. This chapter reviews the different techniques of in silico target profiling and their main applications such as inference of unwanted targets, drug repurposing, or compound prioritization after phenotypic-based screening campaigns.

Synthetic biology (SB) is an emerging discipline, which is slowly reorienting the field of drug discovery. For thousands of years, living organisms such as plants were the major source of human medicines. The difficulty in resynthesizing natural products, however, often turned pharmaceutical industries away from this rich source for human medicine. More recently, progress on transformation through genetic manipulation of biosynthetic units in microorganisms has opened the possibility of in-depth exploration of the large chemical space of natural products derivatives. Success of SB in drug synthesis culminated with the bioproduction of artemisinin by microorganisms, a tour de force in protein and metabolic engineering. Today, synthetic cells are not only used as biofactories but also used as cell-based screening platforms for both target-based and phenotypic-based approaches. Engineered genetic circuits in synthetic cells are also used to decipher disease mechanisms or drug mechanism of actions and to study cell–cell communication within bacteria consortia. This review presents latest developments of SB in the field of drug discovery, including some challenging issues such as drug resistance and drug toxicity.

Computational protein design, a process that searches for mutants with desired improved properties, plays a central role in the conception of many synthetic biology devices including biosensors, bioproduction, or regulation circuits. To that end, a rational workflow for computational protein design is described here consisting of (a) searching in the sequence, structure or chemical spaces for the desired function and associated protein templates; (b) finding the list of potential hot regions to mutate in the parent proteins; and (c) performing in silico screening of mutants with predicted improved properties.