The use of potent and selective chemical tools with well-defined targets can help elucidate biological processes driving phenotypes in phenotypic screens. However, identification of selective compounds en masse to create targeted screening sets is non-trivial. A systematic approach is needed to prioritize probes, which prevents the repeated use of published but unselective compounds. Here we performed a meta-analysis of integrated large-scale, heterogeneous bioactivity data to create an evidence-based, quantitative metric to systematically rank tool compounds for targets. Our tool score (TS) was then tested on hundreds of compounds by assessing their activity profiles in a panel of 41 cell-based pathway assays. We demonstrate that high-TS tools show more reliably selective phenotypic profiles than lower-TS compounds. Additionally we highlight frequently tested compounds that are non-selective tools and distinguish target family polypharmacology from cross-family promiscuity. TS can therefore be used to prioritize compounds from heterogeneous databases for phenotypic screening.

PPAR-δ agonists are known to enhance fatty acid metabolism, preserving glucose and physical endurance and are suggested as candidates for treating metabolic diseases. None have reached the clinic yet. Our Machine Learning algorithm called “Iterative Stochastic Elimination” (ISE) was applied to construct a ligand-based multi-filter ranking model to distinguish between confirmed PPAR-δ agonists and random molecules. Virtual screening of 1.56 million molecules by this model picked ~2500 top ranking molecules. Subsequent docking to PPAR-δ structures was mainly evaluated by geometric analysis of the docking poses rather than by energy criteria, leading to a set of 306 molecules that were sent for testing in vitro. Out of those, 13 molecules were found as potential PPAR-δ agonist leads with EC50 between 4–19 nM and 14 others with EC50 below 10 µM. Most of the nanomolar agonists were found to be highly selective for PPAR-δ and are structurally different than agonists used for model building.

Using a comprehensive chemical genetics approach, we identified a member of the lignan natural product family, HTP-013, which exhibited significant cytotoxicity across various cancer cell lines. Correlation of compound activity across a panel of reporter gene assays suggested the vacuolar-type ATPase (v-ATPase) as a potential target for this compound. Additional cellular studies and a yeast haploinsufficiency screen strongly supported this finding. Competitive photoaffinity labeling experiments demonstrated that the ATP6V0A2 subunit of the v-ATPase complex binds directly to HTP-013, and further mutagenesis library screening identified resistance-conferring mutations in ATP6V0A2. The positions of these mutations suggest the molecule binds a novel pocket within the domain of the v-ATPase complex responsible for proton translocation. While other mechanisms of v-ATPase regulation have been described, such as dissociation of the complex or inhibition by natural products including bafilomycin A1 and concanamycin, this work provides detailed insight into a distinct binding pocket within the v-ATPase complex.

Aberrant expression of the apoptosis inhibitor bcl-2 provides a survival advantage throughout oncogenesis and can facilitate chemotherapeutic resistance in a variety of human cancers. Follicular lymphoma (FL) for example, is characterized by the chromosomal translocation t(14;18), which results in bcl-2 overexpression and initiates lymphomagenesis. Although FL cells possess ample amounts of bcl-2, they respond remarkably well to standard first-round chemotherapy. However, the vast majority of patients relapses and becomes progressively resistant to therapy. We obtained cell lines derived from chemosensitive and chemoresistant FL patients, that are characterized by the chromosomal translocation t(14;18) and expression of bcl-2, to investigate how chemotherapeutic drugs can circumvent bcl-2 anti-apoptotic function and to identify alterations in those pathways that may facilitate resistance to DNA damaging drugs. In chemosensitive FL cells, we found that DNA damaging drugs promote apoptosis through p53-dependent upregulation of the TRAIL-DR5 receptor, resulting in activation of caspase-8 and downstream executioner caspases, thereby evading bcl-2 mediated suppression of apoptosis. Examination of drug resistant FL cell lines revealed that at least two defects in this pathway can contribute to chemotherapeutic resistance: 1. p53 gene mutations that disable the transcriptional response to DNA damaging drugs, including expression of the TRAIL-DR5 receptor, and 2. transcriptional repression of the cell-death executioner enzyme caspase-3.

In recent years great advances have been made in developing targeted cancer therapeutics that produce dramatic responses in a subset of rationally selected patients. The initial breakthrough of the targeted design concept was established by the treatment of chronic myelogenous leukemia with Abl inhibitors and has been expanded to other cancer indications. As a consequence of this early success in CML, the identification and targeting of genetic lesions that confer cancer dependence has become an established strategy for drug discovery efforts. However, this approach has met with mixed degrees of success as confounding factors, such as tumor heterogeneity, have often resulted in partial responses and/or the emergence of resistance when targeted therapies were employed as single agents. To improve the therapeutic benefit in cancer, rationally-devised novel combinations of two or more agents are being explored clinically. To discover combinations that may be more effective therapies, an unbiased, systematic approach was used to identify drug combinations in vitro, using a panel of genetically diverse, and well characterized cell lines from the cancer cell line encyclopedia (CCLE: Barretina et al. Nature 2012). For three cancer indications, all pairwise combinations of 18 selected drugs (both novel inhibitors and standards of care) were tested as dose matrices in a proliferation assay. Synergistic interactions were scored using isobologram/Loewe's excess inhibition and synergistic concentration ranges for each agent were identified. However, the clinical translation of positive combinations from in vitro matrix-based screens into clinically-relevant doses and schedules are challenging, due to host biology, tumor-stroma interactions, and the pharmacokinetic and pharmacodynamics of drug delivery. To explore this higher complexity, we evaluated the in vitro to in vivo translation of drug synergies in immune-compromised mouse tumor xenograft models. To recapitulate the pharmacological combination effects in vivo, mouse pharmacokinetic data and simulation was used to determine single agent doses that would result in the desired compound plasma concentration range and duration of action. Pharmakokinetics, pharmacodynamics, antitumor activity and tolerability of the combinations were then tested in tumor-bearing mice. Observed combination effects in vivo could in some cases be explained by either the expected biological pathway interactions or partially by physiological effects relating to drug-drug interactions. Citation Format: Marion Wiesmann, Mark Stump, Giordano Caponigro, David Duhl, Brant Firestone, Tom Gesner, Bjoern Gruenenfelder, Daniel Alexander Guthy, Jocelyn Holash, Fred King, Joseph Lehar, Christophe Leroy, Manway Liu, Lilli Petruzelli, Dale Porter, Paul McSheehy, Daniel Menezes, Anupama Reddy, Johannes Roesel, Christian Schnell, Timothy R. Smith, Markus Wartmann. Systematic evaluation of drug combinations in vitro and in vivo. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2058. doi:10.1158/1538-7445.AM2013-2058