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An Experimental Pharmacokinetic Computer Program to Predict Potential Drug-Drug Interactions
No full textPublisher's version available from http://aibst.com/pdf/Masimirembwa_TODMJ%5B1%5D.pdf,Polypharmacy as a result of combating co-infections, or combination therapy for better efficacy and reducing
the emergency of drug resistance, is on the increase in the African clinical setting in the advent of HIV/AIDS, and tuberculosis
(TB) co-infections, and increasing incidences of malaria and other tropical infections. The clinicians and pharmacists
are therefore faced with the challenge of prescribing drugs in combinations that are likely to result in severe adverse
effects or compromising treatment success. The aim of this study was, therefore, to develop a simple stand alone or network
based experimental computational tool to assist doctors and pharmacists in detecting drug combinations likely to result
in undesirable metabolism based drug-drug interactions (DDIs) and offer alternate safe prescription options. The
mechanism of most drug-drug interactions is through inhibition and induction of drug metabolising enzymes. Models for
the prediction of reversible and irreversible inhibitors of the major drug metabolising enzyme system, cytochrome P450,
were used in developing the pharmacoinformatic tool. These models enable the prediction of likely in vivo drug-drug interactions
from in vitro data. In vivo drug-drug interaction data from the literature was also loaded into the software to
validate the system and to give clinical guidance on specific drug-drug interactions. In this first phase of the project, focus
was on medications used in the treatment of HIV/AIDS, TB, malaria and other diseases common in Africa. The prototypic
tool was based on a Standard Query Language (SQL) database with DELPHI 6.0 as the user interface. Its user friendly
pages lead the doctor or pharmacist through drug combination entry functions and gives warning if an interaction is likely.
Subsequent actions enable the operator to retrieve more information on the mechanism of interactions, the quantitative
measure of the interaction, access to published abstracts on studies, and possible prescription options to minimise DDIs.
The software currently has data for 50 drugs used in the design and focuses on the treatment of tropical diseases in addition
to classical cases of drug-drug interactions involving other general classes of drugs. The tool can be distributed on
Compaq Disk (CD) and be run on any Personal Computer (PC) on windows. We have successfully developed a pharmacokinetic-
based tool with a potential to assist clinicians and pharmacists in detecting and rationalizing DDIs. The tool has
proved very useful as a teaching tool on DDIs by using the more advanced functions that explore the performance of current
drug-drug interactions prediction models. From the available literature, it is clear that more studies need to be done to
establish the prevalence and mechanisms of DDIs in the treatment of infectious diseases. We are now adding more data,
validating the tool and finally testing the acceptability of this tool among clinicians and pharmacists for routine use
Predicting Potential Cyp450 Enzyme Inhibition Based Drug-Drug Interaction During Drugs Prescription Using a Computer Aid
No full textIntroduction: The increase in the number of drugs on the market and concomitant
treatment of co-infections has increased the potential for drug interactions making it
difficult for healthcare professionals to minimize the potential adverse effects of every
drug. Fortunately, Medical Informatics has been evolving to match this increase in
complexity in medical delivery. Pharmacoinformatics has become particularly relevant in
addressing some of the undesirable effects associated with the increased practice of
polypharmacy. Therefore, the major aim of this study was to develop a computer based
pharmacoinformatic tool for use by clinicians and pharmacists in the prediction of in vivo
drug-drug interactions (DDIs) using in vitro data.
Materials and methods: The prototypic tool was developed using Standard Query
Language (SQL) database and Delphi 6.0 as the programming language. Literature
sources were assembled, both as databases and symposia abstracts, original publications
of drug-enzyme or drug-drug interactions for competitive and mechanism-based
inhibition. Sources with validated in vitro methods and having the following parameters:
inhibition constant (Ki); maximum enzyme velocity (Vmax); substrate concentration
needed to reach half maximal velocity (Km); fraction metabolized by cytochrome P450
(fm) and fraction cleared by cytochrome P450 (fh), were considered. Different plasma
concentrations of the inhibitor available to the enzyme site for interaction were tested
with and without taking into account protein binding. The concentrations included the
average maximum plasma concentration (Cmax) and the estimated of maximum
concentration of the inhibitor at entrance to the liver (Iin.max), both bound and unbound. A
pilot study was carried out among 10 doctors and 10 pharmacists to test the medical
relevance of the tool using a questionnaire with scores ranging from 1 (best) to 6 (worst).
Results and discussion: Various drug combinations were tested. The best predictions of
in vivo drug-drug interactions were achieved when the concentration of inhibitor was set
at the unbound maximum concentration at entrance to the liver enzymes with better
overall geometric mean fold error (GMFE) values of 0.68 and overall root mean square
error (RMSE) of 3.13 without considering mechanism-based inhibition (MBI). There was
improvement in overall GMFE (0.49) and RMSE (1.71) for steady-state unbound Cmax
when MBI was incorporated. A preliminary evaluation of the tool by medical
professionals has highly recommended application in private practice and in academia as
a teaching tool, and with mixed reactions in public sector. The survey recommended that
modifications be made on details captured under product composition.
Conclusion: The pharmacoinformatic tool developed during this work is likely to be well
received by the medical community starting as a teaching tool. More drugs used routinely
need to be added, and a high sample size evaluation of relevance and acceptability
conducted. The predictive capacity of the tool had low levels of bias when the
concentration of inhibitor was set at the unbound maximum concentration at entrance to
the liver enzymes. However more work needs to be done to include Drug-Drug
Interactions (DDIs) due to induction and irreversible enzyme inhibition or through
inhibition of other enzymes not considered in this study
Optimization of 1st-line antituberculosis dosing regimens using a population pharmacokinetic approach: food effects, drug combinations and pharmacological effects
No full textIncludes bibliographical references.The aim of this thesis was to evaluate optimal doses of 1st-line antituberculosis dosing regimens using a population pharmacokinetic approach, quantify food effects, drug combinations and pharmacological effects. The population pharmacokinetics of rifampicin, isoniazid and pyrazinamide in 76 children with tuberculosis were described using a population pharmacokinetic approach, and then Monte Carlo simulation were performed to evaluate adequacy of newly recommended weight band based doses in World Health Organisation (WHO) guidelines. Food effect (breakfast) was evaluated on rifapentine pharmacokinetic data in 35 healthy male volunteers. Effect of co-administered intermittent rifapentine on the pharmacokinetics of moxifloxacin was evaluated in 28 patients with pulmonary tuberculosis, who participated in a multicenter controlled clinical trial evaluating high dose rifapentine in combination with moxifloxacin. The moxifloxacin pharmacokinetic model, together with a previously published ofloxacin pharmacokinetic model, was used to evaluate the efficacy between moxifloxacin and ofloxacin. Furthermore, pharmacokinetic summary variables of rifapentine and moxifloxacin were evaluated as predictors of treatment outcome. Simulations based on the final models suggested that with the new guidelines, and utilizing available paediatric fixed dose combinations, children will receive adequate rifampicin exposures when compared to adults, but with a larger degree of variability. However, pyrazinamide and isoniazid exposures in many children will be lower than in adults. For food effect, all meals compared with the fasting state, high fat meal had the greatest effect on rifapentine oral bioavailability, increasing it by 86%; bulky low-fat, bulky-high-fat, and chicken soup resulted in 33%, 46%, and 49% increases in rifapentine oral bioavailability, respectively. Similar trends were observed for the metabolite 25-desacetyl rifapentine. For drug-resistant tuberculosis, using a target ratio of ≥100 for multidrug-resistant strains (without resistance to injectable agents or fluoroquinolones), the cumulative fraction of response (CFR) was 88% for moxifloxacin and only 43% for ofloxacin. The higher dose of 800 mg moxifloxacin was needed to achieve a CFR target of ≥90%. In terms of drug-interaction, rifapentine increased the clearance of moxifloxacin by 8% during antituberculosis treatment compared to that after treatment completion without rifapentine. Also, the effect moxifloxacin and rifapentine pharmacokinetics indices on outcome treatment outcome support that combined effect of longer treatment duration and higher rifapentine exposures are associated with better treatment response. In summary, the newer WHO doses for children may give lower pyrazinamide and isoniazid exposures in many children than in adults. Meals have a substantial impact on rifapentine exposure. Rifapentine did not result in a clinically significant change in moxifloxacin exposure. Moxifloxacin is more efficacious than ofloxacin in the treatment of MDR-TB. The combined effect of longer treatment duration, higher rifapentine exposures are associated with better treatment outcome, but could not differentiate which major factor needed for favourable outcome
PREDICTING POTENTIAL CYP450 ENZYME INHIBITION BASED DRUG-DRUG INTERACTION DURING DRUGS PRESCRIPTION USING A COMPUTER AID
No full textIntroduction: The increase in the number of drugs on the market and concomitant
treatment of co-infections has increased the potential for drug interactions making it
difficult for healthcare professionals to minimize the potential adverse effects of every
drug. Fortunately, Medical Informatics has been evolving to match this increase in
complexity in medical delivery. Pharmacoinformatics has become particularly relevant in
addressing some of the undesirable effects associated with the increased practice of
polypharmacy. Therefore, the major aim of this study was to develop a computer based
pharmacoinformatic tool for use by clinicians and pharmacists in the prediction of in vivo
drug-drug interactions (DDIs) using in vitro data.
Materials and methods: The prototypic tool was developed using Standard Query
Language (SQL) database and Delphi 6.0 as the programming language. Literature
sources were assembled, both as databases and symposia abstracts, original publications
of drug-enzyme or drug-drug interactions for competitive and mechanism-based
inhibition. Sources with validated in vitro methods and having the following parameters:
inhibition constant (Ki); maximum enzyme velocity (Vmax); substrate concentration
needed to reach half maximal velocity (Km); fraction metabolized by cytochrome P450
(fm) and fraction cleared by cytochrome P450 (fh), were considered. Different plasma
concentrations of the inhibitor available to the enzyme site for interaction were tested
with and without taking into account protein binding. The concentrations included the
average maximum plasma concentration (Cmax) and the estimated of maximum
concentration of the inhibitor at entrance to the liver (Iin.max), both bound and unbound. A
pilot study was carried out among 10 doctors and 10 pharmacists to test the medical
relevance of the tool using a questionnaire with scores ranging from 1 (best) to 6 (worst).
Results and discussion: Various drug combinations were tested. The best predictions of
in vivo drug-drug interactions were achieved when the concentration of inhibitor was set
at the unbound maximum concentration at entrance to the liver enzymes with better
overall geometric mean fold error (GMFE) values of 0.68 and overall root mean square
error (RMSE) of 3.13 without considering mechanism-based inhibition (MBI). There was
improvement in overall GMFE (0.49) and RMSE (1.71) for steady-state unbound Cmax
when MBI was incorporated. A preliminary evaluation of the tool by medical
professionals has highly recommended application in private practice and in academia as
a teaching tool, and with mixed reactions in public sector. The survey recommended that
modifications be made on details captured under product composition.
Conclusion: The pharmacoinformatic tool developed during this work is likely to be well
received by the medical community starting as a teaching tool. More drugs used routinely
need to be added, and a high sample size evaluation of relevance and acceptability
conducted. The predictive capacity of the tool had low levels of bias when the
concentration of inhibitor was set at the unbound maximum concentration at entrance to
the liver enzymes. However more work needs to be done to include Drug-Drug
Interactions (DDIs) due to induction and irreversible enzyme inhibition or through
inhibition of other enzymes not considered in this study
PREDICTING POTENTIAL CYP450 ENZYME INHIBITION BASED DRUG-DRUG INTERACTION DURING DRUGS PRESCRIPTION USING A COMPUTER AID
No full textIntroduction: The increase in the number of drugs on the market and concomitant
treatment of co-infections has increased the potential for drug interactions making it
difficult for healthcare professionals to minimize the potential adverse effects of every
drug. Fortunately, Medical Informatics has been evolving to match this increase in
complexity in medical delivery. Pharmacoinformatics has become particularly relevant in
addressing some of the undesirable effects associated with the increased practice of
polypharmacy. Therefore, the major aim of this study was to develop a computer based
pharmacoinformatic tool for use by clinicians and pharmacists in the prediction of in vivo
drug-drug interactions (DDIs) using in vitro data.
Materials and methods: The prototypic tool was developed using Standard Query
Language (SQL) database and Delphi 6.0 as the programming language. Literature
sources were assembled, both as databases and symposia abstracts, original publications
of drug-enzyme or drug-drug interactions for competitive and mechanism-based
inhibition. Sources with validated in vitro methods and having the following parameters:
inhibition constant (Ki); maximum enzyme velocity (Vmax); substrate concentration
needed to reach half maximal velocity (Km); fraction metabolized by cytochrome P450
(fm) and fraction cleared by cytochrome P450 (fh), were considered. Different plasma
concentrations of the inhibitor available to the enzyme site for interaction were tested
with and without taking into account protein binding. The concentrations included the
average maximum plasma concentration (Cmax) and the estimated of maximum
concentration of the inhibitor at entrance to the liver (Iin.max), both bound and unbound. A
pilot study was carried out among 10 doctors and 10 pharmacists to test the medical
relevance of the tool using a questionnaire with scores ranging from 1 (best) to 6 (worst).
Results and discussion: Various drug combinations were tested. The best predictions of
in vivo drug-drug interactions were achieved when the concentration of inhibitor was set
at the unbound maximum concentration at entrance to the liver enzymes with better
overall geometric mean fold error (GMFE) values of 0.68 and overall root mean square
error (RMSE) of 3.13 without considering mechanism-based inhibition (MBI). There was
improvement in overall GMFE (0.49) and RMSE (1.71) for steady-state unbound Cmax
when MBI was incorporated. A preliminary evaluation of the tool by medical
professionals has highly recommended application in private practice and in academia as
a teaching tool, and with mixed reactions in public sector. The survey recommended that
modifications be made on details captured under product composition.
Conclusion: The pharmacoinformatic tool developed during this work is likely to be well
received by the medical community starting as a teaching tool. More drugs used routinely
need to be added, and a high sample size evaluation of relevance and acceptability
conducted. The predictive capacity of the tool had low levels of bias when the
concentration of inhibitor was set at the unbound maximum concentration at entrance to
the liver enzymes. However more work needs to be done to include Drug-Drug
Interactions (DDIs) due to induction and irreversible enzyme inhibition or through
inhibition of other enzymes not considered in this study
PREDICTING POTENTIAL CYP450 ENZYME INHIBITION BASED DRUG-DRUG INTERACTION DURING DRUGS PRESCRIPTION USING A COMPUTER AID
No full textIntroduction: The increase in the number of drugs on the market and concomitant
treatment of co-infections has increased the potential for drug interactions making it
difficult for healthcare professionals to minimize the potential adverse effects of every
drug. Fortunately, Medical Informatics has been evolving to match this increase in
complexity in medical delivery. Pharmacoinformatics has become particularly relevant in
addressing some of the undesirable effects associated with the increased practice of
polypharmacy. Therefore, the major aim of this study was to develop a computer based
pharmacoinformatic tool for use by clinicians and pharmacists in the prediction of in vivo
drug-drug interactions (DDIs) using in vitro data.
Materials and methods: The prototypic tool was developed using Standard Query
Language (SQL) database and Delphi 6.0 as the programming language. Literature
sources were assembled, both as databases and symposia abstracts, original publications
of drug-enzyme or drug-drug interactions for competitive and mechanism-based
inhibition. Sources with validated in vitro methods and having the following parameters:
inhibition constant (Ki); maximum enzyme velocity (Vmax); substrate concentration
needed to reach half maximal velocity (Km); fraction metabolized by cytochrome P450
(fm) and fraction cleared by cytochrome P450 (fh), were considered. Different plasma
concentrations of the inhibitor available to the enzyme site for interaction were tested
with and without taking into account protein binding. The concentrations included the
average maximum plasma concentration (Cmax) and the estimated of maximum
concentration of the inhibitor at entrance to the liver (Iin.max), both bound and unbound. A
pilot study was carried out among 10 doctors and 10 pharmacists to test the medical
relevance of the tool using a questionnaire with scores ranging from 1 (best) to 6 (worst).
Results and discussion: Various drug combinations were tested. The best predictions of
in vivo drug-drug interactions were achieved when the concentration of inhibitor was set
at the unbound maximum concentration at entrance to the liver enzymes with better
overall geometric mean fold error (GMFE) values of 0.68 and overall root mean square
error (RMSE) of 3.13 without considering mechanism-based inhibition (MBI). There was
improvement in overall GMFE (0.49) and RMSE (1.71) for steady-state unbound Cmax
when MBI was incorporated. A preliminary evaluation of the tool by medical
professionals has highly recommended application in private practice and in academia as
a teaching tool, and with mixed reactions in public sector. The survey recommended that
modifications be made on details captured under product composition.
Conclusion: The pharmacoinformatic tool developed during this work is likely to be well
received by the medical community starting as a teaching tool. More drugs used routinely
need to be added, and a high sample size evaluation of relevance and acceptability
conducted. The predictive capacity of the tool had low levels of bias when the
concentration of inhibitor was set at the unbound maximum concentration at entrance to
the liver enzymes. However more work needs to be done to include Drug-Drug
Interactions (DDIs) due to induction and irreversible enzyme inhibition or through
inhibition of other enzymes not considered in this study
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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