Carol Collins

Adverse health effects of marijuana use.

1. Stein EA, Giugliano RP, Koren MJ, et al. Efficacy and safety of evolocumab (AMG 145), a fully human monoclonal antibody to PCSK9, in hyperlipidaemic patients on various background lipid therapies: pooled analysis of 1359 patients in four phase 2 trials. Eur Heart J 2014 March 4 (Epub ahead of print). 2. Koren MJ, Giugliano RP, Raal FJ, et al. Efficacy and safety of longer-term administration of evolocumab (AMG 145) in patients with hypercholesterolemia: 52-week results from the Open-Label Study of Long-Term Evaluation Against LDL-C (OSLER) randomized trial. Circulation 2014;129:234-43. 3. Dadu RT, Ballantyne CM. Lipid lowering with PCSK9 inhibitors. Nat Rev Cardiol 2014 June 24 (Epub ahead of print). 4. Robinson JG, Nedergaard BS, Rogers WJ, et al. Effect of evolocumab or ezetimibe added to moderateor high-intensity statin therapy on LDL-C lowering in patients with hypercholesterolemia: the LAPLACE-2 randomized clinical trial. JAMA 2014; 311:1870-82.Copyright

Designing mobile support for glycemic control in patients with diabetes

We assessed the feasibility and acceptability of using mobile phones as part of an existing Web-based system for collaboration between patients with diabetes and a primary care team. In design sessions, we tested mobile wireless glucose meter uploads and two approaches to mobile phone-based feedback on glycemic control. Mobile glucose meter uploads combined with graphical and tabular data feedback were the most desirable system features tested. Participants had a mixture of positive and negative reactions to an automated and tailored messaging feedback system for self-management support. Participants saw value in the mobile system as an adjunct to the Web-based program and traditional office-based care. Mobile diabetes management systems may represent one strategy to improve the quality of diabetes care.

Risk and predictability of drug interactions in the elderly.

The issue of drug-drug interactions is particularly relevant for geriatric patients with epilepsy because they are often treated with multiple medications for concurrent diseases such as cardiovascular disease and psychiatric disorders (e.g., dementia and depression). The antidepressants with the least potential for altering antiepileptic drug (AED) metabolism are citalopram, escitalopram, venlafaxine, duloxetine, and mirtazapine. The use of established AEDs with enzyme-inducing properties, such as carbamazepine, phenytoin, and phenobarbital, may be associated with reductions in the levels of drugs such as donepezil, galantamine, and particularly warfarin. Carbamazepine, phenytoin, and phenobarbital have been reported to decrease prothrombin time in patients taking oral anticoagulants, although with phenytoin, an increase in prothrombin time has also been reported. Drugs associated with increased risk of bleeding in patients taking oral anticoagulants include selective serotonin reuptake inhibitors (especially fluoxetine), gemfibrozil, fluvastatin, and lovastatin. Other drugs affected by enzyme inducers include cytochrome P450 3A4 substrates, such as calcium channel blockers (e.g., nimodipine, nilvadipine, nisoldipine, and felodipine) and the 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors atorvastatin, lovastatin, and simvastatin. Although there have been no reports of AEDs altering ticlopidine metabolism, ticlopidine coadministration can result in carbamazepine and phenytoin toxicity. Also, there is a significant risk of elevated levels of carbamazepine when diltiazem and verapamil are administered. In addition, there are case reports of phenytoin toxicity when administered with diltiazem. Drugs with a lower potential for metabolic drug interactions include (1) cholinesterase inhibitors (although the theoretical possibility of a reduction in donepezil and galantamine levels by enzyme-inducing AEDs should be considered) and the N-methyl-D-aspartate receptor antagonist memantine and (2) antihypertensives such as angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, hydrophilic beta-blockers, and thiazide diuretics. There is a moderate risk that enzyme-inducing AEDs will decrease levels of lipophilic beta-blockers. Newer AEDs have a lower potential for drug interactions. In particular, levetiracetam and gabapentin have not been reported to alter enzyme activity. In summary, there is a significant potential for drug interactions between AEDs and drugs commonly prescribed in geriatric patients with epilepsy.

Computing with evidence Part I: A drug-mechanism evidence taxonomy oriented toward confidence assignment.

We present a new evidence taxonomy that, when combined with a set of inclusion criteria, enable drug experts to specify what their confidence in a drug mechanism assertion would be if it were supported by a specific set of evidence. We discuss our experience applying the taxonomy to representing drug-mechanism evidence for 16 active pharmaceutical ingredients including six members of the HMG-CoA-reductase inhibitor family (statins). All evidence was collected and entered into the Drug-Interaction Knowledge Base (DIKB); a system that can provide customized views of a body of drug-mechanism knowledge to users who do not agree about the inferential value of particular evidence types. We provide specific examples of how the DIKBs evidence model can flag when a particular use of evidence should be re-evaluated because its related conjectures are no longer valid. We also present the algorithm that the DIKB uses to identify patterns of evidence support that are indicative of fallacious reasoning by the evidence-base curators.

Computing with evidence Part II: An evidential approach to predicting metabolic drug-drug interactions.

We describe a novel experiment that we conducted with the Drug Interaction Knowledge-base (DIKB) to determine which combinations of evidence enable a rule-based theory of metabolic drug-drug interactions to make the most optimal set of predictions. The focus of the experiment was a group of 16 drugs including six members of the HMG-CoA-reductase inhibitor family (statins). The experiment helped identify evidence-use strategies that enabled the DIKB to predict significantly more interactions present in a validation set than the most rigorous strategy developed by drug experts with no loss of accuracy. The best-performing strategies included evidence types that would normally be of lesser predictive value but that are often more accessible than more rigorous types. Our experimental methods represent a new approach to leveraging the available scientific evidence within a domain where important evidence is often missing or of questionable value for supporting important assertions.

Modeling Drug Mechanism Knowledge Using Evidence and Truth Maintenance

To protect the safety of patients, it is vital that researchers find methods for representing drug mechanism knowledge that support making clinically relevant drug-drug interaction (DDI) predictions. Our research aims to identify the challenges of representing and reasoning with drug mechanism knowledge and to evaluate potential informatics solutions to these challenges through the process of developing a knowledge-based system capable of predicting clinically relevant DDIs that occur via metabolic mechanisms. In previous work, we designed a simple, rule-based, model of metabolic inhibition and induction and applied it to a database containing assertions about 267 drugs. This pilot system taught us that drug mechanism knowledge is often dynamic, missing, or uncertain. In this paper, we propose methods to address these properties of mechanism knowledge and describe a new prototype system, the Drug Interaction Knowledge-base (DIKB), that implements our proposed methods so that we can explore their strengths and limitations. A novel feature of the DIKB is its use of a truth maintenance system to link changes in the evidence support for assertions about drug properties to the set of interactions and non-interactions the system predicts.

Transporter expression in liver tissue from subjects with alcoholic or hepatitis C cirrhosis quantified by targeted quantitative proteomics

Although data are available on the change of expression/activity of drug-metabolizing enzymes in liver cirrhosis patients, corresponding data on transporter protein expression are not available. Therefore, using quantitative targeted proteomics, we compared our previous data on noncirrhotic control livers (n = 36) with the protein expression of major hepatobiliary transporters, breast cancer resistance protein (BCRP), bile salt export pump (BSEP), multidrug and toxin extrusion protein 1 (MATE1), multidrug resistance–associated protein (MRP)2, MRP3, MRP4, sodium taurocholate–cotransporting polypeptide (NTCP), organic anion–transporting polypeptides (OATP)1B1, 1B3, 2B1, organic cation transporter 1 (OCT1), and P-glycoprotein (P-gp) in alcoholic (n = 27) and hepatitis C cirrhosis (n = 30) livers. Compared with control livers, the yield of membrane protein from alcoholic and hepatitis C cirrhosis livers was significantly reduced by 56 and 67%, respectively. The impact of liver cirrhosis on transporter protein expression was transporter-dependent. Generally, reduced protein expression (per gram of liver) was found in alcoholic cirrhosis livers versus control livers, with the exception that the expression of MRP3 was increased, whereas no change was observed for MATE1, MRP2, OATP2B1, and P-gp. In contrast, the impact of hepatitis C cirrhosis on protein expression of transporters (per gram of liver) was diverse, showing an increase (MATE1), decrease (BSEP, MRP2, NTCP, OATP1B3, OCT1, and P-gp), or no change (BCRP, MRP3, OATP1B1, and 2B1). The expression of hepatobiliary transporter protein differed in different diseases (alcoholic versus hepatitis C cirrhosis). Finally, incorporation of protein expression of OATP1B1 in alcoholic cirrhosis into the Simcyp physiologically based pharmacokinetics cirrhosis module improved prediction of the disposition of repaglinide in liver cirrhosis patients. These transporter expression data will be useful in the future to predict transporter-mediated drug disposition in liver cirrhosis patients.

Computing with evidence

We present a new evidence taxonomy that, when combined with a set of inclusion criteria, enable drug experts to specify what their confidence in a drug mechanism assertion would be if it were supported by a specific set of evidence. We discuss our experience applying the taxonomy to representing drug-mechanism evidence for 16 active pharmaceutical ingredients including six members of the HMG-CoA-reductase inhibitor family (statins). All evidence was collected and entered into the Drug-Interaction Knowledge Base (DIKB); a system that can provide customized views of a body of drug-mechanism knowledge to users who do not agree about the inferential value of particular evidence types. We provide specific examples of how the DIKBs evidence model can flag when a particular use of evidence should be re-evaluated because its related conjectures are no longer valid. We also present the algorithm that the DIKB uses to identify patterns of evidence support that are indicative of fallacious reasoning by the evidence-base curators.

Prediction of Maximum Exposure in Poor Metabolizers Following Inhibition of Nonpolymorphic Pathways

Marked increases in exposure of some substrates have been noted in poor metabolizers given inhibitors of nonpolymorphic enzymes. Among the small number of clinical trials conducted to investigate this problem, a wide variation in the degree of maximum exposure ratios (area under the curve in poor metabolizers in the presence of inhibitor/area under the curve in extensive metabolizers) among the different substrates has been reported, with some trials reporting profound increases (> tenfold), and others demonstrating less remarkable changes (< twofold). The conduct of such trials raises safety concerns for the trial participants, in addition to other ethical and logistic concerns; therefore, the possibility was investigated that maximum exposure (area under the curve in poor metabolizers in the presence of an inhibitor) could be predicted, and that substrates susceptible to large increases in exposure could be identified. Existing clinical trials were identified by data mining the literature. A theoretical approach was developed to predict maximum exposure in poor metabolizers from studies in extensive metabolizers treated with an inhibitor of the nonpolymorphic pathway. Maximum exposure was predicted in eleven instances and the mean percentage difference between predicted and observed was 11.9%. Substrates with a fraction of substrate dose metabolized by the polymorphic enzyme (fm(POLY)) higher than 75% are at greater risk of exhibiting maximum exposure ratios of more than tenfold.

Long-acting combination anti-HIV drug suspension enhances and sustains higher drug levels in lymph node cells than in blood cells and plasma.

Objective: The aim of the present study was to determine whether a combination of anti-HIV drugs – tenofovir (TFV), lopinavir (LPV) and ritonavir (RTV) – in a lipid-stabilized nanosuspension (called TLC-ART101) could enhance and sustain intracellular drug levels and exposures in lymph node and blood cells above those in plasma. Design: Four macaques were given a single dose of TLC-ART101 subcutaneously. Drug concentrations in plasma and mononuclear cells of the blood (PBMCs) and lymph nodes (LNMCs) were analysed using a validated combination LC-MS/MS assay. Results: For the two active drugs (TFV, LPV), plasma and PBMC intracellular drug levels persisted for over 2 weeks; PBMC drug exposures were three- to four-fold higher than those in plasma. Apparent terminal half-lives (t1/2) of TFV and LPV were 65.3 and 476.9 h in plasma, and 169.1 and 151.2 h in PBMCs. At 24 and 192 h, TFV and LPV drug levels in LNMCs were up to 79-fold higher than those in PBMCs. Analysis of PBMC intracellular TFV and its active metabolite TFV-diphosphate (TFV-DP) indicated that intracellular exposures of total TFV and TFV-DP were markedly higher and persisted longer than in humans and macaques dosed with oral TFV prodrugs, tenofovir disoproxil fumarate (TDF) or tenofovir alafenamide (TAF). Conclusions: A simple, scalable three-drug combination, lipid-stabilized nanosuspension exhibited persistent drug levels in cells of lymph nodes and the blood (HIV host cells) and in plasma. With appropriate dose adjustment, TLC-ART101 may be a useful HIV treatment with a potential to impact residual virus in lymph nodes.