Numerous injuries and fatalities in chemical laboratories in the United States over the past few decades have suggested the need to take measures that go beyond mere compliance and toward promoting safer practices. A collaboration between the Center for Innovative and Strategic Transformation of Alkane Resources and Purdue Process Safety and Assurance Center assessed the current safety culture in chemical laboratories at their academic and industrial partners by conducting safety surveys. Key areas of improvement were identified from the responses to the safety surveys, which if addressed can mitigate the severity of safety incidents or prevent them from occurring. The findings indicate that a majority of the respondents from academia conduct comprehensive lab safety trainings (∼80%), have standard operating procedures for potentially hazardous activities (∼90%), regularly discuss safety-related issues during lab group meetings (∼85%), or are involved in routine safety inspections (∼85%). However, fewer of the academic respondents were aware of a database for safety incidents in their departments (∼50%) or utilized a standard safety review process for new experimental setups or modifications to existing setups (∼70%). The results from industry respondents suggest that improvements to commonly used hazard evaluation tools and increased accessibility to comprehensive databases can increase the effectiveness of hazard evaluation processes. Additionally, recommended best practices and guidelines are provided for researchers within the scientific community to develop key safety documentation that will both strengthen the safety culture and improve safety performance in their laboratories. Taken together, this safety initiative highlights the much-needed attention and effort that are beneficial to promote improved safety culture within academic and industrial chemical laboratories.

In the past several years, the U.S. Chemical Safety Board has found an increase in the frequency of laboratory accidents and injuries. An independent survey of industrial and academic laboratories by the authors indicated the shortage of documentation on best practices and lack of free and user-friendly risk assessment tools to be some of the key reasons for the occurrence of safety incidents. Thus, development of a framework to document, assess, and mitigate hazards is a critical starting point for ensuring safe laboratory practices. To address this requirement, Reactive Hazards Evaluation Analysis and Compilation Tool (RHEACT), an online platform to compile and scrutinize hazards-related information, was developed. When planning an experiment, the researchers provide RHEACT: (1) information about the chemicals involved in the reaction, in the form of Safety Data Sheets (SDS), and (2) operating parameters of the reaction. Through the user-supplied SDS, an operational hazard matrix and a chemical compatibility matrix are generated. In addition, adiabatic temperature rise of the reaction is estimated to ensure that the chemistry is within user-controlled bounds. The user is provided with a broad initial evaluation of potential hazards and is notified of safety concerns associated with the reaction before conducting the experiment. We believe that this user-friendly online tool will help engender a safer laboratory working environment.

Vincristine is a core chemotherapeutic drug administered to pediatric acute lymphoblastic leukemia patients. Despite its efficacy in treating leukemia, it can lead to severe peripheral neuropathy in a sub-group of the patients. Peripheral neuropathy is a debilitating and painful side-effect that can severely impact an individual’s quality of life. Currently, there are no established predictors of peripheral neuropathy incidence during the early stage of chemotherapeutic treatment. As a result, patients who are not susceptible to peripheral neuropathy may receive sub-therapeutic treatment due to an empirical upper cap on the dose, while others may experience severe neuropathy at the same dose. Contrary to previous genomics based approaches, we employed a metabolomics approach to identify small sets of metabolites that can be used to predict a patient’s susceptibility to peripheral neuropathy at different time points during the treatment. Using those identified metabolites, we developed a novel strategy to predict peripheral neuropathy and subsequently adjust the vincristine dose accordingly. In accordance with this novel strategy, we created a free user-friendly tool, VIPNp , for physicians to easily implement our prediction strategy. Our results showed that focusing on metabolites, which encompasses both genotypic and phenotypic variations, can enable early prediction of peripheral neuropathy in pediatric leukemia patients.

Optimization of the thiol-ene reaction for the preparation of methyl 3-((3,3,3-trifluoropropyl)thio)propanoate (4), a key intermediate in the synthesis of the sap-feeding insecticidal candidate tyclopyrazoflor (1), is described. The major challenge with the radical thiol-ene chemistry was control of the regioselectivity between the linear and branched products. Reducing the radical initiation temperature was found to be the key variable in controlling the selectivity. Because of the high cost and storage challenges associated with the use of the room-temperature diazo initiator 2,2'-azobis(4-methoxy-2,4-dimethylvaleronitrile) (V-70), a two-component initiator system consisting of benzoyl peroxide and N,N-dimethylaniline was developed, allowing for radical initiation at temperatures as low as -15 degrees C. Application of semibatch operation gave 90:1 selectivity favoring the linear product. The overall yield and selectivity of the radical thiol-ene reaction were improved from 78% yield and 11:1 selectivity with azobis(isobutyronitrile) in batch mode to 91% yield and 90:1 selectivity with the two-component system in semibatch mode, further eliminating the need for a fractional distillation purification step.

6-Mercaptopurine (6-MP) is one of the key drugs in the treatment of many pediatric cancers, auto immune diseases and inflammatory bowel disease. 6-MP is a prodrug, converted to an active metabolite 6-thioguanine nucleotide (6-TGN) through enzymatic reaction involving thiopurine methyltransferase (TPMT). Pharmacogenomic variation observed in the TPMT enzyme produces a significant variation in drug response among the patient population. Despite 6-MP's widespread use and observed variation in treatment response, efforts at quantitative optimization of dose regimens for individual patients are limited. In addition, research efforts devoted on pharmacogenomics to predict clinical responses are proving far from ideal. In this work, we present a Bayesian population modeling approach to develop a pharmacological model for 6-MP metabolism in humans. In the face of scarcity of data in clinical settings, a global sensitivity analysis based model reduction approach is used to minimize the parameter space. For accurate estimation of sensitive parameters, robust optimal experimental design based on D-optimality criteria was exploited. With the patient-specific model, a model predictive control algorithm is used to optimize the dose scheduling with the objective of maintaining the 6-TGN concentration within its therapeutic window. More importantly, for the first time, we show how the incorporation of information from different levels of biological chain-of response (i.e. gene expression-enzyme phenotype-drug phenotype) plays a critical role in determining the uncertainty in predicting therapeutic target. The model and the control approach can be utilized in the clinical setting to individualize 6-MP dosing based on the patient's ability to metabolize the drug instead of the traditional standard-dose-for-all approach.

Acute Lymphoblastic Leukemia, commonly known as ALL, is a predominant form of cancer during childhood. With the advent of modern healthcare support, the 5-year survival rate has been impressive in the recent past. However, long-term ALL survivors embattle several treatment-related medical and socio-economic complications due to excessive and inordinate chemotherapy doses received during treatment. In this work, we present a model-based approach to personalize 6-Mercaptopurine (6-MP) treatment for childhood ALL with a provision for incorporating the pharmacogenomic variations among patients. Semi-mechanistic mathematical models were developed and validated for i) 6-MP metabolism, ii) red blood cell mean corpuscular volume (MCV) dynamics, a surrogate marker for treatment efficacy, and iii) leukopenia, a major side-effect. With the constraint of getting limited data from clinics, a global sensitivity analysis based model reduction technique was employed to reduce the parameter space arising from semi-mechanistic models. The reduced, sensitive parameters were used to individualize the average patient model to a specific patient so as to minimize the model uncertainty. Models fit the data well and mimic diverse behavior observed among patients with minimum parameters. The model was validated with real patient data obtained from literature and Riley Hospital for Children in Indianapolis. Patient models were used to optimize the dose for an individual patient through nonlinear model predictive control. The implementation of our approach in clinical practice is realizable with routinely measured complete blood counts (CBC) and a few additional metabolite measurements. The proposed approach promises to achieve model-based individualized treatment to a specific patient, as opposed to a standard-dose-for-all, and to prescribe an optimal dose for a desired outcome with minimum side-effects.

Sickle cell is a hereditary disease affecting more than 100,000 people in the United States alone that causes hemoglobin in red blood cells to polymerize and turn the cell into a sickle shape, resulting in severe vasoocclusive crises and ischemic attacks. Sickle cell patients often suffer from pain crises, with the number of pain crises linked to their prognosis, especially at a younger age. Currently, the drug hydroxyurea (HU) is used to treat the disease, with a measure of red blood cell volume (RBC MCV) as monitor for treatment progression. However, physicians have to wait atleast 120 days to identify treatment efficacy due to the amount of time it takes RBCs to reach a steady state volume after starting treatment. Therefore we propose measuring the volume of reticulocytes (MCVr), immature RBCs in the blood, as a marker for treatment efficacy, as the faster dynamics should allow for measurement of treatment efficacy after only 10 days. Data from 127 patients with various diagnoses (sickle cell, thalassemia, various forms of anaemia) and treatments (hydroxyurea, transfusions, no treatment) were analysed to establish relationships between MCVr and HU treatment, MCVr and RBC MCV, and other factors such as gender and time. The results suggest that there may be a correlation between MCV and MCVr for sickle cell patients treated with hydroxyurea versus other forms of treatment. Therefore, a prospective study should be planned to expand on the findings of this study.

Despite recent advancements in "omic" technologies, personalized medicine has not realized its fullest potential due to isolated and incomplete application of gene expression tools. In many instances, pharmacogenomics is being interchangeably used for personalized medicine, when actually it is one of the many facets of personalized medicine. Herein, we highlight key issues that are hampering the advancement of personalized medicine and highlight emerging predictive tools that can serve as a decision support mechanism for physicians to personalize treatments.

Relentless research efforts in the recent past have led to the discovery of various cancer treatment modalities. However, there is no specific or absolute therapy for cancer treatment. As a result, doctors/researchers are proposing and employing unprecedented and promising strategies that synergistically combine different therapies to cure cancer. The timing and dosage levels at which the different therapies need to be administered for optimally treating cancer remains not only an interesting research problem but also one that is clinically relevant. In this work, we use a mathematical model comprising of ordinary differential equations to study how immunotherapy and chemotherapy can be optimally employed in cancer treatment. The model elucidates the dynamics of tumor cells, immune cells and therapeutic agents. Our aim is to minimize the tumor size via the optimum application of therapeutic agents. To this end, we use a multi-objective optimization strategy to design an immuno-chemotherapy plan that treats the cancer using minimum therapeutic intervention. We will also compare the best possible single therapy results with the best possible combination therapy protocol.

In this paper, a mathematical model based on the diffusion of nutrients is developed by considering the physiological changes accompanying the growth of avascular tumour. Avascular tumour growth involves the formation of three different zones namely proliferation, quiescent and necrotic zones. The main processes on which avascular tumour growth depends are: (i) diffusion of nutrients through the tumour from the contiguous tissues, (ii) consumption rate of the nutrients by the cells in the tumour, and (iii) cell death by apoptosis and necrosis. In the model, we consider the tumour to be spherical and the principal nutrients responsible for its growth are oxygen and glucose. By solving for the concentration profiles using the model developed, we are able to compute the radii of the quiescent and necrotic zones as well as that of the tumour. The proposed model is also validated using in vitro tumour growth data and Gompertzian empirical relationship parameters available in the literature. Our model is also successful in capturing the saturated volume of the avascular tumour for different nutrient concentrations at the tumour surface.