Supplementary Data 3 from Increased Fibroblast Growth Factor-Inducible 14 Expression Levels Promote Glioma Cell Invasion via Rac1 and Nuclear Factor-κB and Correlate with Poor Patient Outcome

A target-based drug discovery strategy has led to a bias away from low molecular weight (MWT) drug discovery. Analysis of the ACS chemistry registration system shows that most low MWT drugs were first made in the time era before target-based drug discovery. Therapeutic activity among most low MWT drugs was identified in the era of phenotypic drug discovery when drugs were selected based on their phenotypic effects and before in vitro screening, mechanism of action considerations and experiences with fragment screening became known. The common perception that drugs cannot be found among low MWT compounds is incorrect based on both drug discovery history and our own experience with MLR-1023. The greater proportion of low MWT compounds that are commercially available compared to higher MWT compounds is a factor that should facilitate biology study. We posit that low MWT compounds are more suited to identification of new therapeutic activity using phenotypic screens provided that the phenotypic screening method has enough screening capacity. On-target and off-target therapeutic activities are discussed from both a chemistry and biology perspective because of a concern that either phenotypic or low MWT drug discovery might bias towards promiscuous compounds that combine on-target and off-target effects. Among ideal drug repositioning candidates (late-stage pre-clinical or clinically-experience compounds), pleiotropic activity (multiple therapeutic actions) is far more likely due to on-target effects arising where a single target mediates multiple therapeutic benefits, a desirable outcome for drug development purposes compared to the off-target alternative. Our exemplar of a low MWT compound, MLR-1023, discovered by phenotypic screening and subsequently found to have a single mechanism of action would have been overlooked based on current era medicinal chemistry precedent. The diverse therapeutic activities described for this compound by us, and others arise from the same pleiotropic lyn kinase activation molecular target. MLR-1023 serves as a proof-of-principle that potent, on target, low MWT drugs can be discovered by phenotypic screening.

The drug discovery and development process is notoriously wrought with a high failure rate. A key contributor to this phenomenon is our significantly incomplete understanding of the biological systems that we are manipulating. We propose that an element of this lack of understanding is the degree to which the therapeutic targets that are modulated by drugs that we work with are involved in more biology, and thereby more therapeutic potential, than most investigators appreciate. This is reflected in the high rate at which drugs are used for indications other than the ones for which they were originally developed. We have coined this phenomenon of multi-therapeutic application for a single drug, pharmacological pleiotropy. MLR-1023, with its activation of Lyn kinase, provides an excellent illustration of pharmacological pleiotropy. Here we provide several examples detailed with scientific understanding across diverse therapeutic space, animal model validation in every case, and with at least two instances of clinical validation. The story also serves as a good example of the fact that there is much more to successful drug discovery and development beyond accomplishing the already arduous task of clinically proving that a drug is safe, well tolerated, and effective for the intended indication.

Drug discovery requires the combination of medicinal chemistry and biology. In this article Chris Lipinski, the medicinal chemist, describes the chemical origins at Pfizer of Tolimidone1 the starting point for the repurposed MLR-1023 (Ochman et al., 2012). Andrew Reaume, the biologist, describes his motivation to develop a high quality (i.e. in vivo model) phenotypic screening platform as an ideal drug repositioning platform.

Increasingly, the pharmaceutical industry has been plagued with escalating costs coupled with decreasing productivity, leading to speculation that the pharmaceutical business model as we know it may be broken. It is in this context that many in the industry have been searching for innovative strategies to reduce cost as well as risk. Both phenotypic screening and drug repositioning represent discovery approaches that fit this description. Melior Discovery is unique among drug discovery organizations in its use of an in vivo phenotypic screening platform used to reposition discontinued clinical-stage compounds. The story of Melior's lead candidate, MLR-1023, illustrates this approach. We show that when dealing with “privileged” substrate (discontinued clinical-stage compounds that exhibit good human safety and tolerability characteristics and other favorable drug-like characteristics), an in vivo screening platform, comprising a wide array of animal models of human disease, is ideal. Many years of conducting these screens on hundreds of compounds has shown the frequency with which otherwise unpredicted therapeutic potential is associated with drug targets that were thought to be well-characterized.

The "rule of 5" has become a mainstay of decision-making in the pharmaceutical industry as well as in nonindustrial (academic and institutional) drug development. However the authors of the original paper never intended for "double cutoffs" to preclude development of new drug leads for parasitic diseases.

BACKGROUND:Physician in triage and rotational patient assignment are different front-end processes that are designed to improve patient flow, but there are little or no data comparing them. OBJECTIVE:To compare physician in triage with rotational patient assignment with respect to multiple emergency department (ED) operational metrics. METHODS:Design-Retrospective cohort review. Patients-Patients seen on 23 days on which we utilized a physician in triage with those patients seen on 23 matched days when we utilized rotational patient assignment. RESULTS:There were 1,869 visits during physician in triage and 1,906 visits during rotational patient assignment. In a simple comparison, rotational patient assignment was associated with a lower median length of stay (LOS) than physician in triage (219 min vs. 233 min; difference of 14 min; 95% confidence interval [CI] 5-27 min). In a multivariate linear regression incorporating multiple confounders, there was a nonsignificant reduction in the geometric mean LOS in rotational patient assignment vs. physician in triage (204 min vs. 217 min; reduction of 6.25%; 95% CI -3.6% to 15.2%). There were no significant differences between groups for left before being seen, left subsequent to being seen, early (within 72 h) returns, early returns with admission, or complaint ratio. CONCLUSIONS:In a single-site study, there were no statistically significant differences in important ED operational metrics between a physician in triage model and a rotational patient assignment model after adjusting for confounders.

The rule of five (Ro5), based on physicochemical profiles of phase II drugs, is consistent with structural limitations in protein targets and the drug target ligands. Three of four parameters in Ro5 are fundamental to the structure of both target and drug binding sites. The chemical structure of the drug ligand depends on the ligand chemistry and design philosophy. Two extremes of chemical structure and design philosophy exist; ligands constructed in the medicinal chemistry synthesis laboratory without input from natural selection and natural product (NP) metabolites biosynthesized based on evolutionary selection. Exceptions to Ro5 are found mostly among NPs. Chemistry chameleon-like behavior of some NPs due to intra-molecular hydrogen bonding as exemplified by cyclosporine A is a strong contributor to NP Ro5 outliers. The fragment derived, drug Navitoclax is an example of the extensive expertise, resources, time and key decisions required for the rare discovery of a non-NP Ro5 outlier.

BACKGROUND:Bioassay data analysis continues to be an essential, routine, yet challenging task in modern drug discovery and chemical biology research. The challenge is to infer reliable knowledge from big and noisy data. Some aspects of this problem are general with solutions informed by existing and emerging data science best practices. Some aspects are domain specific, and rely on expertise in bioassay methodology and chemical biology. Testing compounds for biological activity requires complex and innovative methodology, producing results varying widely in accuracy, precision, and information content. Hit selection criteria involve optimizing such that the overall probability of success in a project is maximized, and resource-wasteful "false trails" are avoided. This "fail-early" approach is embraced both in pharmaceutical and academic drug discovery, since follow-up capacity is resource-limited. Thus, early identification of likely promiscuous compounds has practical value.RESULTS:Here we describe an algorithm for identifying likely promiscuous compounds via associated scaffolds which combines general and domain-specific features to assist and accelerate drug discovery informatics, called Badapple: bioassay-data associative promiscuity pattern learning engine. Results are described from an analysis using data from MLP assays via the BioAssay Research Database (BARD) http://bard.nih.gov. Specific examples are analyzed in the context of medicinal chemistry, to illustrate associations with mechanisms of promiscuity. Badapple has been developed at UNM, released and deployed for public use two ways: (1) BARD plugin, integrated into the public BARD REST API and BARD web client; and (2) public web app hosted at UNM.CONCLUSIONS:Badapple is a method for rapidly identifying likely promiscuous compounds via associated scaffolds. Badapple generates a score associated with a pragmatic, empirical definition of promiscuity, with the overall goal to identify "false trails" and streamline workflows. Unlike methods reliant on expert curation of chemical substructure patterns, Badapple is fully evidence-driven, automated, self-improving via integration of additional data, and focused on scaffolds. Badapple is robust with respect to noise and errors, and skeptical of scanty evidence.

STUDY OBJECTIVE:We compare emergency department (ED) operational metrics obtained in the first year of a rotational patient assignment system (in which patients are assigned to physicians automatically according to an algorithm) with those obtained in the last year of a traditional physician self-assignment system (in which physicians assigned themselves to patients at physician discretion).METHODS:This was a pre-post retrospective study of patients at a single ED with no financial incentives for physician productivity. Metrics of interest were length of stay; arrival-to-provider time; rates of left before being seen, left subsequent to being seen, early returns (within 72 hours), and early returns with admission; and complaint ratio.RESULTS:We analyzed 23,514 visits in the last year of physician self-assignment and 24,112 visits in the first year of rotational patient assignment. Rotational patient assignment was associated with the following improvements (percentage change): median length of stay 232 to 207 minutes (11%), median arrival to provider time 39 to 22 minutes (44%), left before being seen 0.73% to 0.36% (51%), and complaint ratio 9.0/1,000 to 5.4/1,000 (40%). There were no changes in left subsequent to being seen, early returns, or early returns with admission.CONCLUSION:In a single facility, the transition from physician self-assignment to rotational patient assignment was associated with improvement in a broad array of ED operational metrics. Rotational patient assignment may be a useful strategy in ED front-end process redesign.

PURPOSE:We propose a framework with simple proxies to dissect the relative energy contributions responsible for standard drug discovery binding activity.METHODS:We explore a rule of thumb using hydrogen-bond donors, hydrogen-bond acceptors and rotatable bonds as relative proxies for the thermodynamic terms. We apply this methodology to several datasets (e.g., multiple small molecules profiled against kinases, Mycobacterium tuberculosis (Mtb) high throughput screening (HTS) and structure based drug design (SBDD) derived compounds, and FDA approved drugs).RESULTS:We found that Mtb active compounds developed through SBDD methods had statistically significantly larger PEnthalpy values than HTS derived compounds, suggesting these compounds had relatively more hydrogen bond donor and hydrogen bond acceptors compared to rotatable bonds. In recent FDA approved medicines we found that compounds identified via target-based approaches had a more balanced enthalpic relationship between these descriptors compared to compounds identified via phenotypic screensCONCLUSIONS:As it is common to experimentally optimize directly for total binding energy, these computational methods provide alternative calculations and approaches useful for compound optimization alongside other common metrics in available software and databases.

Background: Although the use of a physician and nurse team at triage has been shown to improve emergency department (ED) throughput, the mechanism(s) by which these improvements occur is less clear. Objectives: 1) To describe the effect of a Rapid Medical Assessment (RMA) team on ED length of stay (LOS) and rate of left without being seen (LWBS); 2) To estimate the effect of RMA on different groups of patients. Methods: For Objective 1, we compared LOS and LWBS on dates when we utilized RMA to comparable dates when we did not. For Objective 2, we utilized patient logs to divide patients into groups and estimated the effects of the RMA on each. Results: Objective 1. LOS fell from 297.8 min pre-RMA to 261.7 min during RMA, an improvement of 36.1 (95% confidence interval 21.8-50.4) min; LWBS did not change significantly. Objective 2. Patients seen and dispositioned by the RMA had an estimated decrease in LOS of 117.8 min (estimated decrease in LOS of 45%), but patients seen by the RMA whose care was transitioned to the main ED had an estimated increase in LOS of 25.0 min (estimated increase in LOS of 8%). Conclusions: On a system level, the addition of an RMA shift at a single facility was associated with an improvement in LOS, but not LWBS. On a mechanistic level, it seems that improvements occurred as a result of the rapid disposition component of the RMA rather than placing advanced orders at triage. (C) 2015 Elsevier Inc.

The recent outbreak of the Ebola virus in West Africa has highlighted the clear shortage of broad-spectrum antiviral drugs for emerging viruses. There are numerous FDA approved drugs and other small molecules described in the literature that could be further evaluated for their potential as antiviral compounds. These molecules are in addition to the few new antivirals that have been tested in Ebola patients but were not originally developed against the Ebola virus, and may play an important role as we await an effective vaccine. The balance between using FDA approved drugs versus novel antivirals with minimal safety and no efficacy data in humans should be considered. We have evaluated 55 molecules from the perspective of an experienced medicinal chemist as well as using simple molecular properties and have highlighted 16 compounds that have desirable qualities as well as those that may be less desirable. In addition we propose that a collaborative database for sharing such published and novel information on small molecules is needed for the research community studying the Ebola virus.

Introduction: Background: Program used to enhance teamwork and communication among health professionals to improve patient safety and employee satisfaction. Objective: We hypothesized that Team Strategies and Tools to Enhance Performance and Patient Safety (TeamSTEPPS) training would improve communication between physicians and nurses and between physicians and their patients and family members, and that it would improve patient perceptions of emergency department teamwork. Methods: Design: Before and after prospective observational study. Setting: Tertiary Care Hospital Emergency Department. Participants/Subjects: Twelve core physicians and 43 nurses underwent two, 4-hour TeamSTEPPS training sessions in July 2011 and July 2012. The first session consisted of didactic instruction using the TeamSTEPPS material. The second session was comprised of simulations focusing on the content of the initial training course. Nurses were asked to rate individual physicians on five distinct aspects of communication, both before and after the training sessions. Statistical Methods: Survey results were compared using theWilcoxon signed rank test. Patient satisfaction survey questions regarding teamwork (4th Quarters 2010 and 2011) were analyzed using two-sample t-tests. Results: TeamSTEPPS improved nurse’s perception regarding physician communication with patients and their families (post: 4.28 ± 0.37 vs. pre: 4.16 ± 0.42, p = .0479), with a trend towards improvement in nurse’s perception of physician’s communication with nursing staff regarding changes in patient care plans (post: 3.94 ± 0.38 vs. pre: 3.81 ± 0.5, p = .0942). TeamSTEPPS was also associated with a significant improvement in patient’s rating of teamwork between doctors and nurses as “excellent” (post: 62.9% vs. pre: 48.3%, p = .0132). Conclusions: Team training with the TeamSTEPPS program improved selected aspects of nursing and patient perceptions of teamwork and communication between emergency department physicians and nurses.

The recent outbreak of the Ebola virus in West Africa has highlighted the clear shortage of broad-spectrum antiviral drugs for emerging viruses. There are numerous FDA approved drugs and other small molecules described in the literature that could be further evaluated for their potential as antiviral compounds. These molecules are in addition to the few new antivirals that have been tested in Ebola patients but were not originally developed against the Ebola virus, and may play an important role as we await an effective vaccine. The balance between using FDA approved drugs versus novel antivirals with minimal safety and no efficacy data in humans should be considered. We have evaluated 55 molecules from the perspective of an experienced medicinal chemist as well as using simple molecular properties and have highlighted 16 compounds that have desirable qualities as well as those that may be less desirable. In addition we propose that a collaborative database for sharing such published and novel information on small molecules is needed for the research community studying the Ebola virus.

In a decade with over half a billion dollars of investment, more than 300 chemical probes have been identified to have biological activity through NIH funded screening efforts. We have collected the evaluations of an experienced medicinal chemist on the likely chemistry quality of these probes based on a number of criteria including literature related to the probe and potential chemical reactivity. Over 20% of these probes were found to be undesirable. Analysis of the molecular properties of these compounds scored as desirable suggested higher pK(a), molecular weight, heavy atom count, and rotatable bond number. We were particularly interested whether the human evaluation aspect of medicinal chemistry due diligence could be computationally predicted. We used a process of sequential Bayesian model building and iterative testing as we included additional probes. Following external validation of these methods and comparing different machine learning methods, we identified Bayesian models with accuracy comparable to other measures of drug-likeness and filtering rules created to date.

Views about collaborative drug discovery changed from caution to advocacy in a few years. Milestones were the 2004 NIH Molecular Libraries Screening Center Network and library with screening data deposited to Pubchem; the 2008 Wellcome Trust transfer of proprietary chemogenomic data to the public; and in 2008, the formalization of collaborative efforts in the Clinical and Translational Science Pharmaceutical Assets Award portal. The biology/medicinal chemistry interface is difficult in academic drug discovery, with its wide range in academic drug discovery skills sets. Academics talk about innovation, thinking out of the box, maximum chemical diversity, and not being limited by preconceived rules and filters. Industry people talk about pragmatism, lessons learned, and about worthless screening compounds. Chemistry space errors impede academic drug discovery and collaborations. Screening diverse libraries is the worst way to discover a drug. Biologically active compounds occur in small tight clusters infrequently through chemistry space. Currently, well-known problematic functionality is replaced with more subtle problem compounds. Medicinal chemistry quality suffers when the academic choice is publish or perish. Hypothesis-driven research is a concern for future collaborative drug discovery because biology research driven by hypothesis can often be wrong. Complex natural products cannot be analyzed because the shapes are uncertain. This hinders exploitation of natural products in drug discovery and chemical biology.