Discovery of Novel Liver-Stage Antimalarials through Quantum Similarity.

Without quantum theory any understanding of molecular interactions is incomplete. In principal, chemistry, and even biology, can be fully derived from non-relativistic quantum mechanics. In practice, conventional quantum chemical calculations are computationally too intensive and time consuming to be useful for drug discovery on more than a limited basis. A previously described, original, quantum-based computational process for drug discovery and design bridges this gap between theory and practice, and allows the application of quantum methods to large-scale in silico identification of active compounds. Here, we show the results of this quantum-similarity approach applied to the discovery of novel liver-stage antimalarials. Testing of only five of the model-predicted compounds in vitro and in vivo hepatic stage drug inhibition assays with P. berghei identified four novel chemical structures representing three separate quantum classes of liver-stage antimalarials. All four compounds inhibited liver-stage Plasmodium as a single oral dose in the quantitative PCR mouse liver-stage sporozoites-challenge model. One of the newly identified compounds, cethromycin [ABT-773], a macrolide-quinoline hybrid, is a drug with an extensive (over 5,000 people) safety profile warranting its exploitation as a new weapon for the current effort of malaria eradication. The results of our molecular modeling exceed current state-of-the-art computational methods. Drug discovery through quantum similarity is data-driven, agnostic to any particular target or disease process that can evaluate multiple phenotypic, target-specific, or co-crystal structural data. This allows the incorporation of additional pharmacological requirements, as well as rapid exploration of novel chemical spaces for therapeutic applications.

Discovery of potent, novel Nrf2 inducers via quantum modeling, virtual screening, and in vitro experimental validation.

Nuclear factor erythroid 2-related factor 2 (Nrf2) is the master transcription factor of the antioxidant response element pathway, coordinating the induction of detoxifying and antioxidant enzymes. Nrf2 is normally sequestered in the cytoplasm by Kelch-like ECH-associating protein 1 (Keap1). To identify novel small molecules that will disturb Nrf2-Keap1 binding and promote activation of the Nrf2- antioxidant response element pathway, we generated a quantum model based on the structures of known Nrf2- antioxidant response element activators. We used the quantum model to perform in silico screening on over 18 million commercially available chemicals to identify the structures predicted to activate the Nrf2- antioxidant response element pathway based on the quantum model. The top hits were tested in vitro, and half of the predicted hits activated the Nrf2-antioxidant response element pathway significantly in primary cell culture. In addition, we identified a new family of Nrf2-antioxidant response element-activating structures that all have comparable activity to tBHQ and protect against oxidative stress and dopaminergic toxins in vitro. The improved ability to identify potent activators of Nrf2 through the combination of in silico and in vitro screening described here improves the speed and cost associated with screening Nrf2-antioxidant response element -activating compounds for drug development.

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

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.