Discovery of potent, novel, non-toxic anti-malarial compounds via quantum modelling, virtual screening and in vitro experimental validation

Abstract

Background: Developing resistance towards existing anti-malarial therapies emphasize the urgent need for new therapeutic options. Additionally, many malaria drugs in use today have high toxicity and low therapeutic indices. Gradient Biomodeling, LLC has developed a quantum-model search technology that uses quantum similarity and does not depend explicitly on chemical structure, as molecules are rigorously described in fundamental quantum attributes related to individual pharmacological properties. Therapeutic activity, as well as toxicity and other essential properties can be analysed and optimized simultaneously, independently of one another. Such methodology is suitable for a search of novel, non-toxic, active anti-malarial compounds.

Methods: A set of innovative algorithms is used for the fast calculation and interpretation of electron-density attributes of molecular structures at the quantum level for rapid discovery of prospective pharmaceuticals. Potency and efficacy, as well as additional physicochemical, metabolic, pharmacokinetic, safety, permeability and other properties were characterized by the procedure. Once quantum models are developed and experimentally validated, the methodology provides a straightforward implementation for lead discovery, compound optimizzation and de novo molecular design.

Results: Starting with a diverse training set of 26 well-known anti-malarial agents combined with 1730 moderately active and inactive molecules, novel compounds that have strong anti-malarial activity, low cytotoxicity and structural dissimilarity from the training set were discovered and experimentally validated. Twelve compounds were identified in silico and tested in vitro; eight of them showed anti-malarial activity (IC50 ≤ 10 μM), with six being very effective (IC50 ≤ 1 μM), and four exhibiting low nanomolar potency. The most active compounds were also tested for mammalian cytotoxicity and found to be non-toxic, with a therapeutic index of more than 6,900 for the most active compound.

Conclusions: Gradient's metric modelling approach and electron-density molecular representations can be powerful tools in the discovery and design of novel anti-malarial compounds. Since the quantum models are agnostic of the particular biological target, the technology can account for different mechanisms of action and be used for de novo design of small molecules with activity against not only the asexual phase of the malaria parasite, but also against the liver stage of the parasite development, which may lead to true causal prophylaxis.

Figure 1

Fuzzy decisions tree and network. Quantum attributes are used to represent molecular systems. These attributes define a specific, relevant metric in the modelling structure space and thus allow the proper use of rigorous mathematical techniques. Also, they have strong relationships with chemical features controlling molecular interactions, and well-defined physical properties are often related to a single attribute. In its simplest implementation, the modelling algorithm produces a fuzzy decision tree, which can be generalized to a more powerful fuzzy decision network. Each tree node corresponds to a single attribute (interaction constraint). The attribute explaining most data variance occupies the highest node. In a fully resolved decision tree, terminal nodes contain only either active or inactive molecules. Further, each terminal node is fully characterized statistically by associated confidence intervals and other parameters. A decision network provides a complete characterization of the interaction patterns found within the modelling data.

Figure 2

Anti-malarial training set example structures. The core of the training set used to create the anti-malarial filter consists of 26 known anti-malarial compounds (full list in Table 1).

Figure 3

Quantum anti-malarial model. Starting with known anti-malarial drugs (A), quantum components (QCs) that control anti-malarial activity were identified (red and green shaded areas). Dissimilar nuclear arrangements in active molecules can have similar anti-malarial electron density transforms (EDTs). For example, even though the red-shaded area illustrated as QC2 in panel A is comprised of different atoms in a different chemical substructure, the algorithms calculated their anti-malarial EDTs to be similar to one another. The QCs (B) were calculated and visualized as described in Materials and Methods. The subsequent virtual screen identified novel compounds (C) predicted to be active against P. falciparum based on these pre-computed anti-malarial QCs, i.e., it discovered molecules with novel nuclear arrangements that carry the same anti-malarial QCs (D). Containing two symmetrical quantum components (2 × QC1) which encompass the entire molecule, GR-M009 is the most active novel compound, while the less active GR-M011 contains only one quantum component (QC2). Red dots represent oxygen atoms, dark blue dots represent nitrogen atoms, light blue dots represent carbon atoms, and yellow dots represent sulphur atoms.

Figure 4

Novel anti-malarial compounds identified through quantum metric modelling.