Set-up and maintenance of syringe drivers for subcutaneous drug administration in palliative care

Focal points: Details of syringe driver use were collected from 11 inpatient hospice units in the West Midlands and compared with predetermined standards set by a multidisciplinary group We recorded 294 syringe driver events which complied with standards for the choice of administration site, protection of the syringe driver from light and the reason for initiation of the infusion Issues of nurse training, variability in prescribing practices, provision of written information for patients, labelling and documentation of the diluent used were all identified as areas in which compliance with standards could be improved

Optimization Problem in a Class of Linear System. Application to Multiple Drug Administration

2000 Mathematics Subject Classification: 62H15, 62P10.

An adaptive drug delivery design using neural networks for effective treatment of infectious diseases: A simulation study

An adaptive drug delivery design is presented in this paper using neural networks for effective treatment of infectious diseases. The generic mathematical model used describes the coupled evolution of concentration of pathogens, plasma cells, antibodies and a numerical value that indicates the relative characteristic of a damaged organ due to the disease under the influence of external drugs. From a system theoretic point of view, the external drugs can be interpreted as control inputs, which can be designed based on control theoretic concepts. In this study, assuming a set of nominal parameters in the mathematical model, first a nonlinear controller (drug administration) is designed based on the principle of dynamic inversion. This nominal drug administration plan was found to be effective in curing "nominal model patients" (patients whose immunological dynamics conform to the mathematical model used for the control design exactly. However, it was found to be ineffective in curing "realistic model patients" (patients whose immunological dynamics may have off-nominal parameter values and possibly unwanted inputs) in general. Hence, to make the drug delivery dosage design more effective for realistic model patients, a model-following adaptive control design is carried out next by taking the help of neural networks, that are trained online. Simulation studies indicate that the adaptive controller proposed in this paper holds promise in killing the invading pathogens and healing the damaged organ even in the presence of parameter uncertainties and continued pathogen attack. Note that the computational requirements for computing the control are very minimal and all associated computations (including the training of neural networks) can be carried out online. However it assumes that the required diagnosis process can be carried out at a sufficient faster rate so that all the states are available for control computation.

Effective drug dosage design for treatment of infectious diseases using dynamic inversion

A generic nonlinear mathematical model describing the human immunological dynamics is used to design an effective automatic drug administration scheme. Even though the model describes the effects of various drugs on the dynamic system, this work is confined to the drugs that kill the invading pathogen and heal the affected organ. From a system theoretic point of view, the drug inputs can be interpreted as control inputs, which can be designed based on control theoretic concepts. The controller is designed based on the principle of dynamic inversion and is found to be effective in curing the �nominal model patient� by killing the invading microbes and healing the damaged organ. A major advantage of this technique is that it leads to a closed-form state feedback form of control. It is also proved from a rigorous mathematical analysis that the internal dynamics of the system remains stable when the proposed controller is applied. A robustness study is also carried out for testing the effectiveness of the drug administration scheme for parameter uncertainties. It is observed from simulation studies that the technique has adequate robustness for many �realistic model patients� having off-nominal parameter values as well.

Economics of new oncology drug development.

PURPOSE: Review existing studies and provide new results on the development, regulatory, and market aspects of new oncology drug development. METHODS: We utilized data from the US Food and Drug Administration (FDA), company surveys, and publicly available commercial business intelligence databases on new oncology drugs approved in the United States and on investigational oncology drugs to estimate average development and regulatory approval times, clinical approval success rates, first-in-class status, and global market diffusion. RESULTS: We found that approved new oncology drugs to have a disproportionately high share of FDA priority review ratings, of orphan drug designations at approval, and of drugs that were granted inclusion in at least one of the FDA's expedited access programs. US regulatory approval times were shorter, on average, for oncology drugs (0.5 years), but US clinical development times were longer on average (1.5 years). Clinical approval success rates were similar for oncology and other drugs, but proportionately more of the oncology failures reached expensive late-stage clinical testing before being abandoned. In relation to other drugs, new oncology drug approvals were more often first-in-class and diffused more widely across important international markets. CONCLUSION: The market success of oncology drugs has induced a substantial amount of investment in oncology drug development in the last decade or so. However, given the great need for further progress, the extent to which efforts to develop new oncology drugs will grow depends on future public-sector investment in basic research, developments in translational medicine, and regulatory reforms that advance drug-development science.

Returns to R&D on new drug introductions in the 1980s.

This study finds that the mean IRR for 1980-84 U.S. new drug introductions is 11.1%, and the mean NPV is 22 million (1990 dollars). The distribution of returns is highly skewed. The results are robust to plausible changes in the baseline assumptions. Our work is also compared with a 1993 study by the OTA. Despite some important differences in assumptions, both studies imply that returns for the average NCE are within one percentage point of the industry's cost of capital. This is much less than what is typically observed in analyses based on accounting data.

A Data Mining Approach for the Identification of Adverse Drug Events (ADE) resulting from Drug-Drug Interactions (DDI) to improve pharmacovigilance

In the current study, epidemiology study is done by means of literature survey in groups identified to be at higher potential for DDIs as well as in other cases to explore patterns of DDIs and the factors affecting them. The structure of the FDA Adverse Event Reporting System (FAERS) database is studied and analyzed in detail to identify issues and challenges in data mining the drug-drug interactions. The necessary pre-processing algorithms are developed based on the analysis and the Apriori algorithm is modified to suit the process. Finally, the modules are integrated into a tool to identify DDIs. The results are compared using standard drug interaction database for validation. 31% of the associations obtained were identified to be new and the match with existing interactions was 69%. This match clearly indicates the validity of the methodology and its applicability to similar databases. Formulation of the results using the generic names expanded the relevance of the results to a global scale. The global applicability helps the health care professionals worldwide to observe caution during various stages of drug administration thus considerably enhancing pharmacovigilance

Administration of medication

Recorded in 2011 these narrated slides summarise rules and legislation at time of recordng

The impact of a closed-loop electronic prescribing and administration system on prescribing errors, administration errors and staff time: a before-and-after study

Objectives: To assess the impact of a closed-loop electronic prescribing, automated dispensing, barcode patient identification and electronic medication administration record (EMAR) system on prescribing and administration errors, confirmation of patient identity before administration, and staff time. Design, setting and participants: Before-and-after study in a surgical ward of a teaching hospital, involving patients and staff of that ward. Intervention: Closed-loop electronic prescribing, automated dispensing, barcode patient identification and EMAR system. Main outcome measures: Percentage of new medication orders with a prescribing error, percentage of doses with medication administration errors (MAEs) and percentage given without checking patient identity. Time spent prescribing and providing a ward pharmacy service. Nursing time on medication tasks. Results: Prescribing errors were identified in 3.8% of 2450 medication orders pre-intervention and 2.0% of 2353 orders afterwards (p<0.001; χ2 test). MAEs occurred in 7.0% of 1473 non-intravenous doses pre-intervention and 4.3% of 1139 afterwards (p = 0.005; χ2 test). Patient identity was not checked for 82.6% of 1344 doses pre-intervention and 18.9% of 1291 afterwards (p<0.001; χ2 test). Medical staff required 15 s to prescribe a regular inpatient drug pre-intervention and 39 s afterwards (p = 0.03; t test). Time spent providing a ward pharmacy service increased from 68 min to 98 min each weekday (p = 0.001; t test); 22% of drug charts were unavailable pre-intervention. Time per drug administration round decreased from 50 min to 40 min (p = 0.006; t test); nursing time on medication tasks outside of drug rounds increased from 21.1% to 28.7% (p = 0.006; χ2 test). Conclusions: A closed-loop electronic prescribing, dispensing and barcode patient identification system reduced prescribing errors and MAEs, and increased confirmation of patient identity before administration. Time spent on medication-related tasks increased.

Experience With Lacosamide in a Series of Children With Drug-Resistant Focal Epilepsy

We report our pediatric experience with lacosarnide, a new antiepileptic drug, approved by the US Food and Drug Administration as adjunctive therapy in focal epilepsy in patients more than 17 years old. We retrospectively reviewed charts for lacosamide use and seizure frequency outcome in patients with focal epilepsy (Wilcoxon signed rank test). Sixteen patients (7 boys) were identified (median dose 275 mg daily, 4.7 mg/kg daily; mean age 14.9 years, range 8-21 years). Patients were receiving a median of 2 antiepileptic drugs (interquartile range [IQR] 1.7-3) in addition to having undergone previous epilepsy surgery (n = 3), vagus nerve stimulation (n = 9), and ketogenic diet (n = 3). Causes included structural (encephalomalacia and diffuse encephalitis, 1 each; stroke in 2) and genetic abnormalities (Aarskog and Rett syndromes, 1 each) or cause not known (n = 10). Median seizure frequency at baseline was 57 per month (IQR 7-75), and after a median follow-up of 4 months (range 1-13 months) of receiving lacosamide, it was 12.5 per month (IQR 3-75), (P < 0.01). Six patients (37.5%; 3 seizure free) were classified as having disease that responded to therapy (>= 50% reduction seizure frequency) and 10 as having disease that did not respond to therapy (<50% in 3; increase in 1; unchanged in 6). Adverse events (tics, behavioral disturbance, seizure worsening, and depression with suicidal ideation in 1 patient each) prompted lacosamide discontinuation in 4/16 (25%). This retrospective study of 16 children with drug-resistant focal epilepsy demonstrated good response to adjunctive lacosamide therapy (median seizure reduction of 39.6%; 37.5% with >= 50% seizure reduction) without severe adverse events. (C) 2011 Elsevier Inc. All rights reserved.

Prediction of Drug Dissolution Profiles Using Artificial Neural Networks

This paper investigates the efficacy and reliability of Artificial Neural Networks (ANNs) as an intelligent decision support tool for pharmaceutical product formulation. Two case studies have been employed to evaluate capabilities of the Multilayer Perceptron network in predicting drug dissolution/release profiles. Performances of the network were evaluated using similarity factor (&fnof[sub 2]) — an index recommended by the United States Food and Drug Administration for profile comparison in pharmaceutical research. In addition, the bootstrap method was applied to assess the network prediction reliability by estimating confidence intervals associated with the results. The Multilayer Perceptron network also demonstrated a superior performance in comparison with multiple regression models. The results reveal that the ANN system has potentials to be a decision support tool for profile prediction in pharmaceutical experimentation, and the bootstrap method could be used as a means to assess reliability of the network prediction. [ABSTRACT FROM AUTHOR].

Clinical association between pharmacogenomics and adverse drug reactions

Adverse drug reactions (ADRs) are a major public health concern and cause significant patient morbidity and mortality. Pharmacogenomics is the study of how genetic polymorphisms affect an individual’s response to pharmacotherapy at the level of a whole genome. This article updates our knowledge on how genetic polymorphisms of important genes alter the risk of ADR occurrence after an extensive literature search. To date, at least 244 pharmacogenes identified have been associated with ADRs of 176 clinically used drugs based on PharmGKB. At least 28 genes associated with the risk of ADRs have been listed by the Food and Drug Administration as pharmacogenomic biomarkers. With the availability of affordable and reliable testing tools, pharmacogenomics looks promising to predict, reduce, and minimize ADRs in selected populations.

Plasma concentrations and behavioral, antinociceptive, and physiologic effects of methadone after intravenous and oral transmucosal administration in cats

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Pharmacokinetics of intraosseous and central venous drug delivery during cardiopulmonary resuscitation

We compared the pharmacokinetics of intraosseous (IO) drug delivery via tibia or sternum, with central venous (CV) drug delivery during cardiopulmonary resuscitation (CPR).Methods: CPR of anesthetized KCl arrest swine was initiated 8 min post arrest. Evans blue and indocyanine green, each were simultaneously injected as a bolus with adrenaline through IO sternal and tibial needles, respectively, n = 7. In second group (n = 6) simultaneous IO sternal and IV central venous (CV) injections were made.Results: Peak arterial blood concentrations were achieved faster for sternal IO vs. tibial IO administration (53 +/- 11 s vs. 107 +/- 27 s, p = 0.03). Tibial IO dose delivered was 65% of sternal administration (p = 0.003). Time to peak blood concentration was similar for sternal IO and CV administration (97 +/- 17 s vs. 70 +/- 12 s, respectively; p = 0.17) with total dose delivered of sternal being 86% of the dose delivered via CV (p = 0.22).Conclusions: IO drug administrations via either the sternum or tibia were effective during CPR in anesthetized swine. However, IO drug administration via the sternum was significantly faster and delivered a larger dose. (C) 2011 Elsevier B.V. All rights reserved.