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Objective: To compare and contrast doravirine (DOR) with other agents in the nonnucleoside reverse transcriptase inhibitor (NNRTI) class, review safety and efficacy data from both completed and ongoing clinical trials, and outline the potential place in therapy of DOR. Data Sources: A literature search using the PubMed database (inception to June 2019) was conducted using the search terms HIV, doravirine, non-nucleoside reverse transcriptase inhibitor, NNRTI, and MK-1439. Study Selection and Data Extraction: Clinical data were limited to those published in the English language from phase 2 or 3 clinical trials. Ongoing trials were identified through ClinicalTrials.gov. Data Synthesis: DOR was approved by the US Food and Drug Administration on the strength of 2 phase 3 randomized, double-blind, noninferiority clinical trials with additional studies currently underway examining its utility in other clinical scenarios. Relevance to Patient Care and Clinical Practice: The role of NNRTIs as part of antiretroviral (ARV) therapy has diminished in recent years given the introduction of more tolerable individual ARV agents and regimens. Despite this, new agents are still needed in the therapeutic arena because treatment failure as well as intolerance can still occur with many first-line therapies. The optimal place in therapy of DOR remains to be defined. Conclusions: DOR is a new NNRTI that represents a potential treatment option for treatment-naïve patients, without many of the previously described untoward effects of the NNRTI class.

Indiana University-Purdue University Indianapolis (IUPUI) / Aromatase is the cytochrome P450 enzyme that converts androgens to estrogens. Aromatase is the target of the aromatase inhibitor class of drugs widely used to treat estrogen-mediated conditions including breast cancer. Little is known about the role of this enzyme in drug metabolism or in drug interactions. Since this lack of knowledge has been an impediment to optimal therapy, it is important to understand these roles of aromatase. Therefore, a comprehensive series of studies was carried out to characterize its ability to metabolize drugs and its susceptibility to inhibition by xenobiotics. The overall objective of this work was to better understand the interactions of small molecules with aromatase and to use this new knowledge to predict aromatase-mediated drug interactions and anticipate novel molecular structures that interact with the enzyme. Aromatase was shown to be a drug metabolizing enzyme able to metabolize methadone both in vitro (Km of 314 μM) and in vivo (22% of methadone clearance). A number of novel aromatase inhibitors that employ diverse kinetic mechanisms were identified. These include a potent competitive inhibitor: norendoxifen (Ki of 35 nM), two non-competitive inhibitors: endoxifen (Ki of 4.0 μM) and N-desmethyl-tamoxifen (Ki of 15.9 μM), a mechanism-based inhibitor: methadone (KI of 40.6 ± 2.8 μM; kinact of 0.061 ± 0.001 min-1), and a stereoselective inhibitor: naringenin (IC50s of 2.8 μM for (R)-enatiomer and 1.4 μM for (S)-enatiomer). Through investigation of the structure-potency relationships so discovered, a series of new biochemical structures to be exploited as aromatase inhibitors were identified. These studies have identified new roles for aromatase as a catalyst for methadone metabolism and as a mediator of the effects of tamoxifen by demonstrating that a number of its metabolites can act as aromatase inhibitors. This work also provides a new mechanistic framework for the design of novel aromatase inhibitors that can be used in breast cancer. Overall, the data suggest ways to more consistently treat breast cancer with current medications, to better anticipate drug interactions, and therefore to improve the quality of life of patients in ways that minimize side effects, while optimizing therapeutic benefits, in each person treated.

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The human gut microbiota contributes to host metabolic processes. Diverse microbial metabolic enzymes can affect therapeutic agents, resulting in chemical modifications that alter drug efficacy and toxicology. These interactions may result in ineffective treatments and dose-limiting side effects, as shown by bacterial modifications of the cardiac drug digoxin and chemotherapy drug irinotecan, respectively. Yet, few drug-microbiome interactions have been characterized. Here, a platform is developed to screen for drug-microbiome interactions, validated by the isolation of a gut bacterium capable of inactivating the antineoplastic drug doxorubicin. Two hundred gut strains isolated from a healthy patient fecal sample were cultured in the presence of antibiotic and antineoplastic drugs to enrich for resistance and possible inactivation. Raoultella planticola was identified for its ability to inactivate doxorubicin anaerobically through whole cell and crude lysate assays. This activity was also observed in other Enterobacteriaceae and resulted in doxorubicin inactivation by the removal of its daunosamine sugar, likely mediated by a molybdopterin-dependent enzyme. Other potential drug-microbiome interactions were identified in this screen and can be analyzed further. This platform enables the identification of drug-microbiome interactions that can be used to study drug pharmacology, improve the efficacy of therapeutic treatments, and advance personalized medicine. / Thesis / Bachelor of Science (BSc) / The collection of microbes in the human intestinal tract, referred to as the gut microbiome, can modify therapeutic agents and change the efficacy of drug treatments. Identifying these interactions between drugs and the microbiome will help the study of drug metabolism, provide explanations for treatment failure, and enable more personalized health care. For this project, a platform was developed to isolate gut bacteria from human fecal samples and characterize bacteria that are capable of inactivating various antibiotics and anticancer drugs. Through this platform, the gut bacterium Raoultella planticola was found to inactivate doxorubicin, a commonly used anticancer drug. These results suggest that doxorubicin may be inactivated in the gut and demonstrates how this platform can be used to identify drug-microbiome interactions.

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A medical expert system for predicting qualitative pharmacodynamic interactions of the cardiovascular system is described. TLCM traces causal paths of drug action through up to three levels of drug action. The three levels which are molecular/receptor level, physiological level and clinical level provide both deep and shallow reasoning in order to overcome the problem of unknowns in medical expert systems. Sparsity of information in pharmacology results from necessity of using non-invasive techniques for monitoring drug effects in the human subject and difficulty in isolating effect from feedback. The qualitative nature of TLCM is another attempt to deal with incomplete information in pharmacology. / Master of Science

Indiana University-Purdue University Indianapolis (IUPUI) / Pharmacodynamics (PD) studies the relationship between drug concentration and drug effect on target sites. This field has recently gained attention as studies involving PD Drug-Drug interactions (DDI) assure discovery of multi-targeted drug agents and novel efficacious drug combinations. A PD drug combination could be synergistic, additive or antagonistic depending upon the summed effect of the drug combination at a target site. The PD literature has grown immensely and most of its knowledge is dispersed across different scientific journals, thus the manual identification of PD DDI is a challenge. In order to support an automated means to extract PD DDI, we propose Pharmacodynamics Miner (PD-Miner). PD-Miner is a text-mining tool, which is capable of identifying PD DDI from in vitro PD experiments. It is powered by two major features, i.e., collection of full text articles and in vitro PD ontology. The in vitro PD ontology currently has four classes and more than hundred subclasses; based on these classes and subclasses the full text corpus is annotated. The annotated full text corpus forms a database of articles, which can be queried based upon drug keywords and ontology subclasses. Since the ontology covers term and concept meanings, the system is capable of formulating semantic queries. PD-Miner extracts in vitro PD DDI based upon references to cell lines and cell phenotypes. The results are in the form of fragments of sentences in which important concepts are visually highlighted. To determine the accuracy of the system, we used a gold standard of 5 expert curated articles. PD-Miner identified DDI with a recall of 75% and a precision of 46.55%. Along with the development of PD Miner, we also report development of a semantically annotated in vitro PD corpus. This corpus includes term and sentence level annotations and serves as a gold standard for future text mining.

Patients on anti-retroviral (ARV) drug treatment are sometimes simultaneously taking other prescribed drugs and/or over-the-counter drugs and/or herbal remedies. Pharmacokinetic drug-drug or herb-drug interactions can occur in these patients, which might be synergistic or antagonistic in nature leading to increased or decreased bioavailability of the ARV. Consequences of bioavailability changes may either be adverse effects due to increased plasma levels, or lack of pharmacological responses due to decreased plasma levels. The aim of this study is to determine if pharmacokinetic interactions exist between selected commercially available herbal products, namely Linctagon Forte®, Viral Choice® and Canova® and the ARV, indinavir, in terms of transport and metabolism in cell culture models. Bi-directional transport of indinavir was evaluated across Caco-2 cell monolayers in four experimental groups, namely indinavir alone (200 μM, negative control group), indinavir in combination with Linctagon Forte®, indinavir in combination with Viral Choice® and indinavir in combination with Canova® at three different concentrations. Verapamil (100 μM), a known P-gp inhibitor, was combined with indinavir in the positive control group. Samples obtained from the transport studies were analysed by means of a validated high performance liquid chromatography (HPLC) method. The apparent permeability coefficient (Papp) values were calculated from the transport results in both directions and the efflux ratio (ER) values were calculated from these Papp values. The metabolism of indinavir was determined in LS180 cells in the same groups as mentioned for the transport study but with ketoconazole (40 μM), a known CYP3A4 inhibitor, as the positive control group. Indinavir and its predominant metabolite (M6) were analysed in the metabolism samples by means of liquid chromatography linked to mass spectroscopy (LC/MS/MS) to determine the effect of the herbal products on the biotransformation of indinavir. The BL-AP transport of indinavir increased in a concentration dependent way in the presence of Linctagon Forte® and Viral Choice® when compared to that of indinavir alone (control group). Canova® only slightly affected the efflux of indinavir compared to that of the control group. Noticeable increases in the efflux ratio values of indinavir were found for Linctagon Forte® and Viral Choice®, whilst the effect of Canova® on the efflux ratio value was negligible. There was a pronounced inhibition of the metabolism of indinavir in LS180 cells over the entire concentration range for all the herbal products investigated in this study. These in vitro pharmacokinetic interactions indicate the selected herbal products may affect indinavir’s bioavailability, but the clinical significance needs to be confirmed with in vivo studies before final conclusions can be made. / MSc (Pharmaceutics), North-West University, Potchefstroom Campus, 2015