Exploring the known chemical space of the plant kingdom: insights into taxonomic patterns, knowledge gaps, and bioactive regions.

Plants are one of the primary sources of natural products for drug development. However, despite centuries of research, only a limited region of the phytochemical space has been studied. To understand the scope of what is explored versus unexplored in the phytochemical space, we begin by reconstructing the known chemical space of the plant kingdom, mapping the distribution of secondary metabolites, chemical classes, and plants traditionally used for medicinal purposes (i.e., medicinal plants) across various levels of the taxonomy. We identify hotspot taxonomic clades occupied by a large proportion of medicinal plants and characterized secondary metabolites, as well as clades requiring further characterization with regard to their chemical composition. In a complementary analysis, we build a chemotaxonomy which has a high level of concordance with the taxonomy at the genus level, highlighting the close relationship between chemical profiles and evolutionary relationships within the plant kingdom. Next, we delve into regions of the phytochemical space with known bioactivity that have been used in modern drug discovery. While we find that the vast majority of approved drugs from phytochemicals are derived from known medicinal plants, we also show that medicinal and non-medicinal plants do not occupy distinct regions of the known phytochemical landscape and their phytochemicals exhibit properties similar to bioactive compounds. Moreover, we also reveal that only a few thousand phytochemicals have been screened for bioactivity and that there are hundreds of known bioactive compounds present in both medicinal and non-medicinal plants, suggesting that non-medicinal plants also have potential therapeutic applications. Overall, these results support the hypothesis that there are many plants with medicinal properties awaiting discovery.

Predicting Childhood Obesity Based on Single and Multiple Well-Child Visit Data Using Machine Learning Classifiers.

Childhood obesity is a public health concern in the United States. Consequences of childhood obesity include metabolic disease and heart, lung, kidney, and other health-related comorbidities. Therefore, the early determination of obesity risk is needed and predicting the trend of a child's body mass index (BMI) at an early age is crucial. Early identification of obesity can lead to early prevention. Multiple methods have been tested and evaluated to assess obesity trends in children. Available growth charts help determine a child's current obesity level but do not predict future obesity risk. The present methods of predicting obesity include regression analysis and machine learning-based classifications and risk factor (threshold)-based categorizations based on specific criteria. All the present techniques, especially current machine learning-based methods, require longitudinal data and information on a large number of variables related to a child's growth (e.g., socioeconomic, family-related factors) in order to predict future obesity-risk. In this paper, we propose three different techniques for three different scenarios to predict childhood obesity based on machine learning approaches and apply them to real data. Our proposed methods predict obesity for children at five years of age using the following three data sets: (1) a single well-child visit, (2) multiple well-child visits under the age of two, and (3) multiple random well-child visits under the age of five. Our models are especially important for situations where only the current patient information is available rather than having multiple data points from regular spaced well-child visits. Our models predict obesity using basic information such as birth BMI, gestational age, BMI measures from well-child visits, and gender. Our models can predict a child's obesity category (normal, overweight, or obese) at five years of age with an accuracy of 89%, 77%, and 89%, for the three application scenarios, respectively. Therefore, our proposed models can assist healthcare professionals by acting as a decision support tool to aid in predicting childhood obesity early in order to reduce obesity-related complications, and in turn, improve healthcare.

Optimizing the Medication Distribution Process for Inpatient Units.

Pharmacy robots and automated dispensing cabinets are commonly used to distribute medications to inpatient units efficiently and safely. Decisions regarding the use of these technologies are often made without full knowledge regarding system effects. This paper determines a cost effective and safe way to distribute medications to patients across a hospital system by minimizing the distribution cost and missing dose rate. A mathematical model is formulated which captures key aspects of the pharmacy distribution process to determine a primary pathway to distribute each medication and dose type to each unit. The model focuses on three primary distribution pathways: cart fill via pharmacy robot, cart fill via pharmacy technician, and automated dispensing cabinets. The problem is solved using a complete year of data from the Geisinger Medical Center. The model results demonstrate the trade-off between pharmacy technician and nurse workload and missing dose rates that occur as hospitals move from a centralized pharmacy to automated dispensing cabinets. These results demonstrate the importance of evaluating the labor effort and missing dose rates when determining the best method to distribute medication.

Structure-based, multi-targeted drug discovery approach to eicosanoid inhibition: Dual inhibitors of mPGES-1 and 5-lipoxygenase activating protein (FLAP).

BACKGROUND: Due to the importance of both prostaglandins (PGs) and leukotrienes (LTs) as pro-inflammatory mediators, and the potential for eicosanoid shunting in the presence of pathway target inhibitors, we have investigated an approach to inhibiting the formation of both PGs and LTs as part of a multi-targeted drug discovery effort. METHODS: We generated ligand-protein X-ray crystal structures of known inhibitors of microsomal prostaglandin E2 synthase-1 (mPGES-1) and the 5-Lipoxygenase Activating Protein (FLAP), with their respective proteins, to understand the overlapping pharmacophores. We subsequently used molecular modeling and structure-based drug design (SBDD) to identify hybrid structures intended to inhibit both targets. RESULTS: This work enabled the preparation of compounds 4 and 5, which showed potent in vitro inhibition of both targets. SIGNIFICANCE: Our findings enhance the structural understanding of mPGES-1 and FLAP's unique ligand binding pockets and should accelerate the discovery of additional dual inhibitors for these two important integral membrane protein drug targets.

Drug Induced Liver Injury (DILI). Mechanisms and Medicinal Chemistry Avoidance/Mitigation Strategies.

Adverse drug reactions (ADRs) are a common cause of attrition in drug discovery and development and drug-induced liver injury (DILI) is a leading cause of preclinical and clinical drug terminations. This perspective outlines many of the known DILI mechanisms and assessment methods used to evaluate and mitigate DILI risk. Literature assessments and retrospective analyses using verified DILI-associated drugs from the Liver Tox Knowledge Base (LTKB) have been used to derive the predictive value of each end point, along with combination approaches of multiple methods. In vitro assays to assess inhibition of the bile salt export pump (BSEP), mitotoxicity, reactive metabolite (RM) formation, and hepatocyte cytolethality, along with physicochemical properties and clinical dose provide useful DILI predictivity. This Perspective also highlights some of the strategies used by medicinal chemists to reduce DILI risk during the optimization of drug candidates.

Using Mathematical Modeling to Improve the Emergency Department Nurse-Scheduling Process.

INTRODUCTION: Nurse scheduling within an emergency department can be a very time-consuming process as nursing leadership works to reach sufficient nurse-staffing levels across every day of the schedule while also working to satisfy nurse preferences. METHODS: A mathematical model is formulated to determine nursing shifts to minimize the number of shifts across a day while accounting for staffing level requirements, nurse preferences, and meal breaks. RESULTS: A daily schedule based on nursing shifts was created and used within the self-scheduling process. Implementing the schedule improved nurse-staffing levels while decreasing the time necessary to reconcile the monthly schedule, resulting in the potential to increase nurse satisfaction. DISCUSSION: The emergency department can use mathematical modeling to improve the nurse-scheduling process.

Identification and Mitigation of Reactive Metabolites of 2-Aminoimidazole-Containing Microsomal Prostaglandin E Synthase-1 Inhibitors Terminated Due to Clinical Drug-Induced Liver Injury.

Two 2-aminoimidazole-based inhibitors, LY3031207 (1) and LY3023703 (2), of the microsomal prostaglandin E synthase-1 (mPGES-1) enzyme were found to cause drug-induced liver injury (DILI) in humans. We studied imidazole ring substitutions to successfully mitigate reactive metabolite (RM) formation. These studies support the conclusion that RM formation may play a role in the observations of DILI and the consideration of 2-aminoimidazoles as structure alerts, due to the high likelihood of bioactivation to generate RMs.

Assessing need for pharmacist involvement to improve care coordination for patients on LAI antipsychotics transitioning from hospital to home: A work system approach.

BACKGROUND: The use of Long-Acting Injectable (LAI) antipsychotic medications has increased for patients with Serious Mental Illness (SMI). Care coordination for this population is complex, and pharmacist involvement may improve and support long-term medication adherence and patient outcomes. OBJECTIVES: (1) Examine pharmacists' role in addressing care coordination and adherence challenges for patients taking Long-Acting Injectable (LAI) antipsychotics; and (2) explore patients' medication use experiences with LAI antipsychotics and educational needs. METHODS: This project utilized a holistic work systems approach to assess the usefulness of implementing a pharmacist-led intervention to improve care coordination for patients taking LAI antipsychotics. Data collection and analyses were guided by the Systems Engineering Initiative for Patient Safety (SEIPS) model. Data were collected using interviews with healthcare team members and patients taking LAI antipsychotics and retrospective chart reviews at a psychiatric hospital in Southwestern Pennsylvania. Data collection elicited information about LAI care coordination, the pharmacist's role, and patients' experiences. Content and thematic analyses were conducted to identify opportunities to improve quality of care and patient outcomes. RESULTS: Sixteen healthcare team members and six patients were interviewed. Twenty patient charts were reviewed to examine the care coordination process. Four themes of the workflow process emerged: pharmacist consultation, in-hospital LAI administration, discharge planning, and outpatient treatment. Key challenges identified included inadequate communication, limited knowledge, and the need for standardized roles. Most patients did not know the name of their LAI antipsychotic and did not recall receiving medication counseling, but were interested in discussing medication concerns with pharmacists. CONCLUSIONS: There is a need for improved communication during LAI care coordination, targeted education for healthcare team members, and standardization of roles. Many patients did not have adequate LAI antipsychotic knowledge or receive appropriate medication counseling. Increased pharmacist involvement in the care coordination process may promote adherence and optimal management of SMI.

Discovery and characterization of [(cyclopentyl)ethyl]benzoic acid inhibitors of microsomal prostaglandin E synthase-1.

We describe a novel class of acidic mPGES-1 inhibitors with nanomolar enzymatic and human whole blood (HWB) potency. Rational design in conjunction with structure-based design led initially to the identification of anthranilic acid 5, an mPGES-1 inhibitor with micromolar HWB potency. Structural modifications of 5 improved HWB potency by over 1000×, reduced CYP2C9 single point inhibition, and improved rat clearance, which led to the selection of [(cyclopentyl)ethyl]benzoic acid compound 16 for clinical studies. Compound 16 showed an IC80 of 24nM for inhibition of PGE2 formation in vitro in LPS-stimulated HWB. A single oral dose resulted in plasma concentrations of 16 that exceeded its HWB IC80 in both rat (5mg/kg) and dog (3mg/kg) for over twelve hours.

Targeting the Nerve Growth Factor (NGF) Pathway in Drug Discovery. Potential Applications to New Therapies for Chronic Pain.

The neurotrophin nerve growth factor (NGF) has been implicated as a key mediator of chronic pain. NGF binds the tropomysin receptor kinase A (TrkA) and p75, resulting in the activation of downstream signaling pathways that have been linked to pro-nociception. While anti-NGF antibodies have demonstrated analgesia both preclinically and in patients, the mechanism of action of these agents remains unclear. We describe ligands targeting NGF, its receptors, and downstream/related targets. This Perspective highlights large and small molecule approaches to targeting the NGF-TrkA pathway both extra- and intracellularly. In addition, we present a strategic framework for future drug discovery efforts in this pathway beyond the targeting of NGF or its receptors. While existing tools have greatly informed NGF-mediated signaling, ongoing and future pathway research may help focus new drug discovery efforts on key novel targets and mechanisms. This may result in highly differentiated therapeutics with greater efficacy and/or improved safety profiles.

Novel Autotaxin Inhibitors for the Treatment of Osteoarthritis Pain: Lead Optimization via Structure-Based Drug Design.

In an effort to develop a novel therapeutic agent aimed at addressing the unmet need of patients with osteoarthritis pain, we set out to develop an inhibitor for autotaxin with excellent potency and physical properties to allow for the clinical investigation of autotaxin-induced nociceptive and neuropathic pain. An initial hit identification campaign led to an aminopyrimidine series with an autotaxin IC50 of 500 nM. X-ray crystallography enabled the optimization to a lead compound that demonstrated favorable potency (IC50 = 2 nM), PK properties, and a robust PK/PD relationship.

Pharmacological Characterization of a Potent Inhibitor of Autotaxin in Animal Models of Inflammatory Bowel Disease and Multiple Sclerosis.

Autotaxin is a secreted enzyme that catalyzes the conversion of lysophosphatidyl choline into the bioactive lipid mediator lysophosphatidic acid (LPA). It is the primary enzyme responsible for LPA production in plasma. It is upregulated in inflammatory conditions and inhibition of autotaxin may have anti-inflammatory activity in a variety of inflammatory diseases. To determine the role of autotaxin and LPA in the pathophysiology of inflammatory disease states, we used a potent and orally bioavailable inhibitor of autotaxin that we have recently identified, and characterized it in mouse models of inflammation, inflammatory bowel disease (IBD), multiple sclerosis (MS), and visceral pain. Compound-1, a potent inhibitor of autotaxin with an IC50 of ∼2 nM, has good oral pharmacokinetic properties in mice and results in a substantial inhibition of plasma LPA that correlates with drug exposure levels. Treatment with the inhibitor resulted in significant anti-inflammatory and analgesic effects in the carrageenan-induced paw inflammation and acetic acid-induced visceral pain tests, respectively. Compound-1 also significantly inhibited disease activity score in the dextran sodium sulfate-induced model of IBD, and in the experimental autoimmune encephalomyelitis model of MS. In conclusion, the present study demonstrates the anti-inflammatory and analgesic properties of a novel inhibitor of autotaxin that may serve as a therapeutic option for IBD, MS, and pain associated with inflammatory states.

Characterization of 3,3-dimethyl substituted N-aryl piperidines as potent microsomal prostaglandin E synthase-1 inhibitors.

Here we report on novel, potent 3,3-dimethyl substituted N-aryl piperidine inhibitors of microsomal prostaglandin E synthases-1(mPGES-1). Example 14 potently inhibited PGE2 synthesis in an ex vivo human whole blood (HWB) assay with an IC50 of 7nM. In addition, 14 had no activity in human COX-1 or COX-2 assays at 30µM, and failed to inhibit human mPGES-2 at 62.5µM in a microsomal prep assay. These data are consistent with selective mPGES-1-mediated reduction of PGE2. In dog, 14 had oral bioavailability (74%), clearance (3.62mL/(min*kg)) and volume of distribution (Vd,ss=1.6L/kg) values within our target ranges. For these reasons, 14 was selected for further study.

Identification of potent and selective retinoic acid receptor gamma (RARγ) antagonists for the treatment of osteoarthritis pain using structure based drug design.

A series of triaryl pyrazoles were identified as potent pan antagonists for the retinoic acid receptors (RARs) α, ß and γ. X-ray crystallography and structure-based drug design were used to improve selectivity for RARγ by targeting residue differences in the ligand binding pockets of these receptors. This resulted in the discovery of novel antagonists which maintained RARγ potency but were greater than 500-fold selective versus RARα and RARß. The potent and selective RARγ antagonist LY2955303 demonstrated good pharmacokinetic properties and was efficacious in the MIA model of osteoarthritis-like joint pain. This compound demonstrated an improved margin to RARα-mediated adverse effects.

Coverage models to determine outreach vaccination center locations in low and middle income countries.

The Expanded Programme on Immunization (EPI) was established in 1974 to ensure that children all around the world benefit from life-saving vaccines. However, in many low and middle income countries, it is extremely difficult to vaccinate the entire population with the standard regimen of vaccines. One important reason for this is geographically dispersed or nomadic populations. To improve vaccination rates, these countries typically use outreach, where health workers take vaccines to remote locations. Outreach is the last, critical link in the vaccine supply chain, and the locations selected to offer outreach directly impact the number of additional children that can be vaccinated. This research presents four quantitative models that can be used to optimize the selection of outreach locations, in order to maximize the number of residents that can be reached; each model addresses a different type of coverage possibility. The models are analyzed and contrasted using an example with inputs generated from a subset of data from the state of Bihar in India that was made available to the authors.

The impact of implementing a demand forecasting system into a low-income country's supply chain.

OBJECTIVE: To evaluate the potential impact and value of applications (e.g. adjusting ordering levels, storage capacity, transportation capacity, distribution frequency) of data from demand forecasting systems implemented in a lower-income country's vaccine supply chain with different levels of population change to urban areas. MATERIALS AND METHODS: Using our software, HERMES, we generated a detailed discrete event simulation model of Niger's entire vaccine supply chain, including every refrigerator, freezer, transport, personnel, vaccine, cost, and location. We represented the introduction of a demand forecasting system to adjust vaccine ordering that could be implemented with increasing delivery frequencies and/or additions of cold chain equipment (storage and/or transportation) across the supply chain during varying degrees of population movement. RESULTS: Implementing demand forecasting system with increased storage and transport frequency increased the number of successfully administered vaccine doses and lowered the logistics cost per dose up to 34%. Implementing demand forecasting system without storage/transport increases actually decreased vaccine availability in certain circumstances. DISCUSSION: The potential maximum gains of a demand forecasting system may only be realized if the system is implemented to both augment the supply chain cold storage and transportation. Implementation may have some impact but, in certain circumstances, may hurt delivery. Therefore, implementation of demand forecasting systems with additional storage and transport may be the better approach. Significant decreases in the logistics cost per dose with more administered vaccines support investment in these forecasting systems. CONCLUSION: Demand forecasting systems have the potential to greatly improve vaccine demand fulfilment, and decrease logistics cost/dose when implemented with storage and transportation increases. Simulation modeling can demonstrate the potential health and economic benefits of supply chain improvements.

Identification and Characterization of Novel Microsomal Prostaglandin E Synthase-1 Inhibitors for Analgesia.

Prostaglandin (PG) E2 plays a critical role in eliciting inflammation. Nonsteroidal anti-inflammatory drugs and selective inhibitors of cyclooxygenase, which block PGE2 production, have been used as key agents in treating inflammation and pain associated with arthritis and other conditions. However, these agents have significant side effects such as gastrointestinal bleeding and myocardial infarction, since they also block the production of prostanoids that are critical for other normal physiologic functions. Microsomal prostaglandin E2 synthase-1 is a membrane-bound terminal enzyme in the prostanoid pathway, which acts downstream of cyclooxygenase 2 and is responsible for PGE2 production during inflammation. Thus, inhibition of this enzyme would be expected to block PGE2 production without inhibiting other prostanoids and would provide analgesic efficacy without the side effects. In this report, we describe novel microsomal prostaglandin E2 synthase-1 inhibitors that are potent in blocking PGE2 production and are efficacious in a guinea pig monoiodoacetate model of arthralgia. These molecules may be useful in treating the signs and symptoms associated with arthritis.

Discovery and Characterization of 2-Acylaminoimidazole Microsomal Prostaglandin E Synthase-1 Inhibitors.

As part of a program aimed at the discovery of antinociceptive therapy for inflammatory conditions, a screening hit was found to inhibit microsomal prostaglandin E synthase-1 (mPGES-1) with an IC50 of 17.4 µM. Structural information was used to improve enzyme potency by over 1000-fold. Addition of an appropriate substituent alleviated time-dependent cytochrome P450 3A4 (CYP3A4) inhibition. Further structure-activity relationship (SAR) studies led to 8, which had desirable potency (IC50 = 12 nM in an ex vivo human whole blood (HWB) assay) and absorption, distribution, metabolism, and excretion (ADME) properties. Studies on the formulation of 8 identified 8·H3PO4 as suitable for clinical development. Omission of a lipophilic portion of the compound led to 26, a readily orally bioavailable inhibitor with potency in HWB comparable to celecoxib. Furthermore, 26 was selective for mPGES-1 inhibition versus other mechanisms in the prostanoid pathway. These factors led to the selection of 26 as a second clinical candidate.

Landscaping the structures of GAVI country vaccine supply chains and testing the effects of radical redesign.

BACKGROUND: Many of the world's vaccine supply chains do not adequately provide vaccines, prompting several questions: how are vaccine supply chains currently structured, are these structures closely tailored to individual countries, and should these supply chains be radically redesigned? METHODS: We segmented the 57 GAVI-eligible countries' vaccine supply chains based on their structure/morphology, analyzed whether these segments correlated with differences in country characteristics, and then utilized HERMES to develop a detailed simulation model of three sample countries' supply chains and explore the cost and impact of various alternative structures. RESULTS: The majority of supply chains (34 of 57) consist of four levels, despite serving a wide diversity of geographical areas and population sizes. These four-level supply chains loosely fall into three clusters [(1) 18 countries relatively more bottom-heavy, i.e., many more storage locations lower in the supply chain, (2) seven with relatively more storage locations in both top and lower levels, and (3) nine comparatively more top-heavy] which do not correlate closely with any of the country characteristics considered. For all three cluster types, our HERMES modeling found that simplified systems (a central location shipping directly to immunization locations with a limited number of Hubs in between) resulted in lower operating costs. CONCLUSION: A standard four-tier design template may have been followed for most countries and raises the possibility that simpler and more tailored designs may be warranted.

Modular vaccine packaging increases packing efficiency.

BACKGROUND: Within a typical vaccine supply chain, vaccines are packaged into individual cylindrical vials (each containing one or more doses) that are bundled together in rectangular "inner packs" for transport via even larger groupings such as cold boxes and vaccine carriers. The variability of vaccine inner pack and vial size may hinder efficient vaccine distribution because it constrains packing of cold boxes and vaccine carriers to quantities that are often inappropriate or suboptimal in the context of country-specific vaccination guidelines. METHODS: We developed in Microsoft Excel (Microsoft Corp., Redmond, WA) a spreadsheet model that evaluated the impact of different packing schemes for the Benin routine regimen plus the introduction of the Rotarix vaccine. Specifically, we used the model to compare the current packing scheme to that of a proposed modular packing scheme. RESULTS: Conventional packing of a Dometic RCW25 that aims to maximize fully-immunized children (FICs) results in 123 FICs and a packing efficiency of 81.93% compared to a maximum of 155 FICs and 94.1% efficiency for an alternative modular packaging system. CONCLUSIONS: Our analysis suggests that modular packaging systems could offer significant advantages over conventional vaccine packaging systems with respect to space efficiency and potential FICs, when they are stored in standard vaccine carrying devices. This allows for more vaccines to be stored within the same volume while also simplifying the procedures used by field workers to pack storage devices. Ultimately, modular packaging systems could be a simple way to help increase vaccine coverage worldwide.