1Hit and Lead Identification: Efficient Practices for Drug Discovery

Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma

BACKGROUND

Cancer patients with advanced disease routinely exhaust available clinical regimens and lack actionable genomic medicine results, leaving a large patient population without effective treatments options when their disease inevitably progresses. To address the unmet clinical need for evidence-based therapy assignment when standard clinical approaches have failed, we have developed a probabilistic computational modeling approach which integrates molecular sequencing data with functional assay data to develop patient-specific combination cancer treatments.

METHODS

Tissue taken from a murine model of alveolar rhabdomyosarcoma was used to perform single agent drug screening and DNA/RNA sequencing experiments; results integrated via our computational modeling approach identified a synergistic personalized two-drug combination. Cells derived from the primary murine tumor were allografted into mouse models and used to validate the personalized two-drug combination. Computational modeling of single agent drug screening and RNA sequencing of multiple heterogenous sites from a single patient's epithelioid sarcoma identified a personalized two-drug combination effective across all tumor regions. The heterogeneity-consensus combination was validated in a xenograft model derived from the patient's primary tumor. Cell cultures derived from human and canine undifferentiated pleomorphic sarcoma were assayed by drug screen; computational modeling identified a resistance-abrogating two-drug combination common to both cell cultures. This combination was validated in vitro via a cell regrowth assay.

RESULTS

Our computational modeling approach addresses three major challenges in personalized cancer therapy: synergistic drug combination predictions (validated in vitro and in vivo in a genetically engineered murine cancer model), identification of unifying therapeutic targets to overcome intra-tumor heterogeneity (validated in vivo in a human cancer xenograft), and mitigation of cancer cell resistance and rewiring mechanisms (validated in vitro in a human and canine cancer model).

CONCLUSIONS

These proof-of-concept studies support the use of an integrative functional approach to personalized combination therapy prediction for the population of high-risk cancer patients lacking viable clinical options and without actionable DNA sequencing-based therapy.

Translational CNS medicines research

The major imperative of the pharmaceutical industry is to effectively translate insights gained from basic research into new medicines. This task is toughest for CNS disorders. Compared with non-CNS drugs, CNS drugs take longer to get to market and their attrition rate is greater. This is principally because of the complexity of the human brain (the cause of many brain disorders remains unknown), the liability of CNS drugs to cause CNS side effects (which limits their use) and the requirement of CNS medicines to cross the blood-CNS barrier (BCNSB) (which restricts their ability to interact with their CNS target). In this review we consider the factors that are important in translating neuroscience research into CNS medicines.

Integration of small-molecule discovery in academic biomedical research

Rapid advances in biomedical sciences in recent years have drastically accelerated the discovery of the molecular basis of human diseases. The great challenge is how to translate the newly acquired knowledge into new medicine for disease prevention and treatment. Drug discovery is a long and expensive process, and the pharmaceutical industry has not been very successful at it, despite its enormous resources and spending on the process. It is increasingly realized that academic biomedical research institutions ought to be engaged in early-stage drug discovery, especially when it can be coupled to their basic research. To leverage the productivity of new-drug development, a substantial acceleration in validation of new therapeutic targets is required, which would require small molecules that can precisely control target functions in complex biological systems in a temporal and dose-dependent manner. In this review, we describe a process of integration of small-molecule discovery and chemistry in academic biomedical research that will ideally bring together the elements of innovative approaches to new molecular targets, existing basic and clinical research, screening infrastructure, and synthetic and medicinal chemistry to follow up on small-molecule hits. Such integration of multidisciplinary resources and expertise will enable academic investigators to discover novel small molecules that are expected to facilitate their efforts in both mechanistic research and new-drug target validation. More broadly academic drug discovery should contribute new entities to therapy for intractable human diseases, especially for orphan diseases, and hopefully stimulate and synergize with the commercial sector.

3-Benzyl-1,3-oxazolidin-2-ones as mGluR2 positive allosteric modulators: Hit-to lead and lead optimization

The discovery, synthesis and SAR of a novel series of 3-benzyl-1,3-oxazolidin-2-ones as positive allosteric modulators (PAMs) of mGluR2 is described. Expedient hit-to-lead work on a single HTS hit led to the identification of a ligand-efficient and structurally attractive series of mGluR2 PAMs. Human microsomal clearance and suboptimal physicochemical properties of the initial lead were improved to give potent, metabolically stable and orally available mGluR2 PAMs.

The influence of lead discovery strategies on the properties of drug candidates

Despite the widespread acceptance of guidelines related to desirable physicochemical properties of potential small-molecule drugs, key properties - such as lipophilicity - of recently developed clinical candidates and advanced lead compounds have been shown to differ significantly from those of historical leads and drugs. By analysing the physicochemical properties of a large database of hits and corresponding leads identified in the past decade, we show that this undesirable phenomenon can be traced back to the nature of high-throughput screening hits and hit-to-lead optimization practices. Conceptual and organizational adjustments may be required to enable a smooth lead-evolution process that reduces the chance of high compound-related attrition in clinical trials.

Algorithmic guided screening of drug combinations of arbitrary size for activity against cancer cells

The standard treatment for most advanced cancers is multidrug therapy. Unfortunately, combinations in the clinic often do not perform as predicted. Therefore, to complement identifying rational drug combinations based on biological assumptions, we hypothesized that a functional screen of drug combinations, without limits on combination sizes, will aid the identification of effective drug cocktails. Given the myriad possible cocktails and inspired by examples of search algorithms in diverse fields outside of medicine, we developed a novel, efficient search strategy called Medicinal Algorithmic Combinatorial Screen (MACS). Such algorithms work by enriching for the fitness of cocktails, as defined by specific attributes through successive generations. Because assessment of synergy was not feasible, we developed a novel alternative fitness function based on the level of inhibition and the number of drugs. Using a WST-1 assay on the A549 cell line, through MACS, we screened 72 combinations of arbitrary size formed from a 19-drug pool across four generations. Fenretinide, suberoylanilide hydroxamic acid, and bortezomib (FSB) was the fittest. FSB performed up to 4.18 SD above the mean of a random set of cocktails or "too well" to have been found by chance, supporting the utility of the MACS strategy. Validation studies showed FSB was inhibitory in all 7 other NSCLC cell lines tested. It was also synergistic in A549, the one cell line in which this was evaluated. These results suggest that when guided by MACS, screening larger drug combinations may be feasible as a first step in combination drug discovery in a relatively small number of experiments.