Rationalizing Counterion Selection for the Development of Lipophilic Salts: A Case Study with Venetoclax.

The use of lipid-based formulations (LBFs) can be hindered by low dose loading due to solubility limitations of candidate drugs in lipid vehicles. Formation of lipophilic salts through pairing these drugs with a lipophilic counterion has been demonstrated as a potential means to enhance dose loading in LBFs. This study investigated the screening of appropriate counterions to form lipophilic salts of the BCS class IV drug venetoclax. The physical properties, lipid solubility, and in vitro performance of the salts were analyzed. This study illustrated the versatility of alkyl sulfates and sulfonates as suitable counterions in lipophilic salt synthesis with up to ∼9-fold higher solubility in medium- and long-chain LBFs when compared to that of the free base form of venetoclax. All salts formulated as LBFs displayed superior in vitro performance when compared to the free base form of the drug due to the higher initial drug loadings in LBFs and increased affinity for colloidal species. Further, in vitro studies confirmed that venetoclax lipophilic salt forms using alkyl chain counterions demonstrated comparable in vitro performance to venetoclax docusate, thus reducing the potential for laxative effects related to docusate administration. High levels of the initial dose loading of venetoclax lipophilic salts were retained in a molecularly dispersed state during dispersion and digestion of the formulation, while also demonstrating increased levels of saturation in biorelevant media. The findings of this study suggest that alkyl chain sulfates and sulfonates can act as a suitable alternative counterion to docusate, facilitating the selection of counterions that can unlock the potential to formulate venetoclax as an LBF.

Exploring career choices of pharmacy graduates over 15 years: A cross-sectional evaluation.

INTRODUCTION: Career opportunities for pharmacists beyond those commonly associated with the degree continue to emerge. A paucity of literature regarding evaluation of pharmacy graduate career paths over extended periods is apparent. Considering international pharmacy workforce capacity pressures, the primary study aim was to evaluate trends in career paths of pharmacy graduates. METHODS: This study utilised a multimethod approach to access graduate career data using publicly accessible information from LinkedIn® profiles and an online survey. The survey was distributed to all pharmacy graduates of a university (2007-2022). Data from both methods was combined, cross-checked, coded and analysed quantitatively using descriptive and inferential statistics. RESULTS: Data from 69.7% of the university's pharmacy graduates was collected. Community pharmacy was the most prevalent employment sector (47.7%), followed by industry (21.5%) and hospital (17.7%). A higher proportion of more recent graduates (≤5 years post-graduation) work in a community or hospital pharmacy role versus those who graduated greater than five years ago (χ2 = 8.44, df = 2, p < 0.05). Post-graduate education was undertaken by 41.3% of graduates. Career satisfaction was high (88.2%) but was lower (χ2 = 11.31, df = 1, p < 0.05) for those in community and hospital (82%) versus other sectors (97.5%). CONCLUSION: This study provides the first analysis of graduate career paths over an extended period, highlighting a novel approach to track pharmacist workforce. While almost two thirds of pharmacy graduates occupy community or hospital roles, a trend of leaving these settings five years post-graduation was evident. Accordingly, this work represents a springboard for additional research to inform future pharmacist workforce planning worldwide.

Mycobacterium tuberculosis complex whole-genome sequencing in New York State: Implementation of a reduced phenotypic drug susceptibility testing algorithm.

Whole-genome sequencing (WGS) can predict drug resistance and antimicrobial susceptibility in Mycobacterium tuberculosis complex (MTBC) and has shown promise in partially replacing culture-based phenotypic drug susceptibility testing (pDST). We performed a two-year side by side study comparing the prediction of drug resistance and antimicrobial susceptibility by WGS molecular DST (mDST) to pDST to determine resistance at the critical concentration by Mycobacterial Growth Indicator Tube (MGIT) and agar proportion testing. Negative predictive values of WGS results were consistently high for the first-line drugs: rifampin (99.9%), isoniazid (99.0%), pyrazinamide (98.5%), and ethambutol (99.8%); the rates of resistance to these drugs, among strains in our population, are 2.9%, 10.4%, 46.3%, and 2.3%, respectively. WGS results were available an average 8 days earlier than first-line MGIT pDST. Based on these findings, we implemented a new testing algorithm with an updated WGS workflow in which strains predicted pan-susceptible were no longer tested by pDST. This algorithm was applied to 1177 isolates between October 2018 and September 2020, eliminating pDST for 66.6% of samples and reducing pDST for an additional 22.0%. This algorithm change resulted in faster turnaround times and decreased cost while maintaining comprehensive antimicrobial susceptibility profiles of all culture-positive MTBC cases in New York.

Direct detection of drug-resistant Mycobacterium tuberculosis using targeted next generation sequencing.

Mycobacterium tuberculosis complex (MTBC) infections are treated with combinations of antibiotics; however, these regimens are not as efficacious against multidrug and extensively drug resistant MTBC. Phenotypic (growth-based) drug susceptibility testing on slow growing bacteria like MTBC requires many weeks to months to complete, whereas sequencing-based approaches can predict drug resistance (DR) with reduced turnaround time. We sought to develop a multiplexed, targeted next generation sequencing (tNGS) assay that can predict DR and can be performed directly on clinical respiratory specimens. A multiplex PCR was designed to amplify a group of thirteen full-length genes and promoter regions with mutations known to be involved in resistance to first- and second-line MTBC drugs. Long-read amplicon libraries were sequenced with Oxford Nanopore Technologies platforms and high-confidence resistance mutations were identified in real-time using an in-house developed bioinformatics pipeline. Sensitivity, specificity, reproducibility, and accuracy of the tNGS assay was assessed as part of a clinical validation study. In total, tNGS was performed on 72 primary specimens and 55 MTBC-positive cultures and results were compared to clinical whole genome sequencing (WGS) performed on paired patient cultures. Complete or partial susceptibility profiles were generated from 82% of smear positive primary specimens and the resistance mutations identified by tNGS were 100% concordant with WGS. In addition to performing tNGS on primary clinical samples, this assay can be used to sequence MTBC cultures mixed with other mycobacterial species that would not yield WGS results. The assay can be effectively implemented in a clinical/diagnostic laboratory with a two to three day turnaround time and, even if batched weekly, tNGS results are available on average 15 days earlier than culture-derived WGS results. This study demonstrates that tNGS can reliably predict MTBC drug resistance directly from clinical specimens or cultures and provide critical information in a timely manner for the appropriate treatment of patients with DR tuberculosis.

Public perceptions of pharmacogenomic services in Ireland - Are people with chronic disease more likely to want service availability than those without? A questionnaire study.

Background: As pharmacogenomic services begin to emerge in primary care, the insight of the public is crucial for its integration into clinical practice. Objectives: To establish perceptions of pharmacogenomics (awareness, understanding, openness to availability, perceived benefits and concerns, willingness to pay, and service setting) and investigate if they differ between those with and without chronic disease(s). Methods: An anonymous, online questionnaire generated using Qualtrics® and circulated via social media and posters placed in eight participating community pharmacies was conducted with Irish adults. The questions were designed to consider existing literature on patient perceptions of pharmacogenomics. Descriptive statistics were used to summarize questionnaire responses. Chi-square test was used to compare categorical variables, while independent sample t-test and one-way ANOVA were used to compare the mean values of two (with and without chronic disease) and three groups (multimorbidity (two or more chronic conditions) and polypharmacy (prescribed four or more regular medicines) (MMPP), a single chronic disease, and those without existing medical conditions) respectively Logistic regression was used to evaluate age and gender adjusted associations of chronic disease(s) with responses. A p-value <0.05 was considered statistically significant. Results: A total of 421 responses were received, 30% (n = 120) of whom reported having a chronic disease. Overall, respondents reported low awareness (44%, n = 166) and poor knowledge (55%, n = 212) of pharmacogenomics. After explaining pharmacogenomics to respondents, patients with chronic disease(s) were 2.17 times more likely (p < 0.001) to want pharmacogenomic services availability than those without existing conditions, adjusted for age and gender (driven by preferences of those with MMPP than those with single chronic disease). Respondents demonstrated a high level of interest and noted both the potential benefits and downsides of pharmacogenomic testing. Willingness-to-pay was not associated with having a chronic disease and respondents were more positive about primary care (community pharmacy or general practice) rather than hospital-based pharmacogenomics implementation. Conclusion: The Irish public in general and those with chronic disease in particular are strongly supportive of pharmacogenomic testing, highlighting an unmet need for its incorporation in medicines optimization. These data underline the need for more research on the implementation of community-based pharmacogenomics services for MMPP patients and ubiquitous pharmacogenomics education programs.

Novel Mycobacterium tuberculosis Complex Genotype Related to M. caprae.

We report the unusual genotypic characterization of a bacterium isolated from a clinical sample of a patient who grew up in Bangladesh and lives in the United States. Using whole-genome sequencing, we identified the bacterium as a member of the Mycobacterium tuberculosis complex (MTBC). Phylogenetic placement of this strain suggests a new MTBC genotype. Even though it had the same spoligotype as M. caprae strains, single-nucleotide polymorphism-based phylogenetic analysis placed the isolate as a sister lineage distinct from M. caprae, most closely related to 5 previously sequenced genomes isolated from primates and elephants in Asia. We propose a new animal-associated lineage, La4, within MTBC.

The Okur-Chung Neurodevelopmental Syndrome Mutation CK2K198R Leads to a Rewiring of Kinase Specificity.

Okur-Chung Neurodevelopmental Syndrome (OCNDS) is caused by heterozygous mutations to the CSNK2A1 gene, which encodes the alpha subunit of protein kinase CK2. The most frequently occurring mutation is lysine 198 to arginine (K198R). To investigate the impact of this mutation, we first generated a high-resolution phosphorylation motif of CK2WT, including the first characterization of specificity for tyrosine phosphorylation activity. A second high resolution motif representing CK2K198R substrate specificity was also generated. Here we report the impact of the OCNDS associated CK2K198R mutation. Contrary to prior speculation, the mutation does not result in a complete loss of function, but rather shifts the substrate specificity of the kinase. Broadly speaking the mutation leads to 1) a decreased preference for acidic residues in the +1 position, 2) a decreased preference for threonine phosphorylation, 3) an increased preference for tyrosine phosphorylation, and 4) an alteration of the tyrosine phosphorylation specificity motif. To further investigate the result of this mutation we have developed a probability-based scoring method, allowing us to predict shifts in phosphorylation in the K198R mutant relative to the wild type kinase. As an initial step we have applied the methodology to the set of axonally localized ion channels in an effort to uncover potential alterations of the phosphoproteome associated with the OCNDS disease condition.

Pharmacogenetic interventions to improve outcomes in patients with multimorbidity or prescribed polypharmacy: a systematic review.

Conventional medicines optimisation interventions in people with multimorbidity and polypharmacy are complex and yet limited; a more holistic and integrated approach to healthcare delivery is required. Pharmacogenetics has potential as a component of medicines optimisation. Studies involving multi-medicine pharmacogenetics in adults with multimorbidity or polypharmacy, reporting on outcomes derived from relevant core outcome sets, were included in this systematic review. Narrative synthesis was undertaken to summarise the data; meta-analysis was inappropriate due to study heterogeneity. Fifteen studies of diverse design and variable quality were included. A small, randomised study involving pharmacist-led medicines optimisation, including pharmacogenetics, suggests this approach could have significant benefits for patients and health systems. However, due to study design heterogeneity and the quality of the included studies, it is difficult to draw generalisable conclusions. Further pragmatic, robust pharmacogenetics studies in diverse, real-world patient populations, are required to establish the benefit of multi-medicine pharmacogenetic screening on patient outcomes.

Machine learning methods for prediction of food effects on bioavailability: A comparison of support vector machines and artificial neural networks.

Despite countless advances in recent decades across various in vitro, in vivo and in silico tools, anticipation of whether a drug will show a human food effect (FE) remains challenging. One means to predict potential FE involves probing any dependence between FE and drug properties. Accordingly, this study explored the potential for two machine learning (ML) algorithms to predict likely FE. Using a collated database of drugs licensed from 2016-2020, drugs were classified into three groups; positive, negative or no FE. Greater than 250 drug properties were predicted for each drug which were used to train predictive models using Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms. When compared, ANN outperformed SVM for FE classification upon training (82%, 72%) and testing (72%, 69%). Both models demonstrated higher FE prediction accuracy than the Biopharmaceutics Classification System (BCS) (46%). This exploratory work provided new insights into the connection between FE and drug properties as the Octanol Water Partition Coefficient (S+logP), Number of Hydrogen Bond Donors (HBD), Topological Polar Surface Area (T_PSA) and Dose (mg) were all significant for prediction. Overall, this study demonstrated the utility of ML to facilitate early anticipation of likely FE in pre-clinical development using four well-known drug properties.

Best practices in current models mimicking drug permeability in the gastrointestinal tract - An UNGAP review.

The absorption of orally administered drug products is a complex, dynamic process, dependant on a range of biopharmaceutical properties; notably the aqueous solubility of a molecule, stability within the gastrointestinal tract (GIT) and permeability. From a regulatory perspective, the concept of high intestinal permeability is intrinsically linked to the fraction of the oral dose absorbed. The relationship between permeability and the extent of absorption means that experimental models of permeability have regularly been used as a surrogate measure to estimate the fraction absorbed. Accurate assessment of a molecule's intestinal permeability is of critical importance during the pharmaceutical development process of oral drug products, and the current review provides a critique of in vivo, in vitro and ex vivo approaches. The usefulness of in silico models to predict drug permeability is also discussed and an overview of solvent systems used in permeability assessments is provided. Studies of drug absorption in humans are an indirect indicator of intestinal permeability, but both in vitro and ex vivo tools provide initial screening approaches and are important tools for assessment of permeability in drug development. Continued refinement of the accuracy of in silico approaches and their validation with human in vivo data will facilitate more efficient characterisation of permeability earlier in the drug development process and will provide useful inputs for integrated, end-to-end absorption modelling.

Artificial Neural Networks to Predict the Apparent Degree of Supersaturation in Supersaturated Lipid-Based Formulations: A Pilot Study.

In response to the increasing application of machine learning (ML) across many facets of pharmaceutical development, this pilot study investigated if ML, using artificial neural networks (ANNs), could predict the apparent degree of supersaturation (aDS) from two supersaturated LBFs (sLBFs). Accuracy was compared to partial least squares (PLS) regression models. Equilibrium solubility in Capmul MCM and Maisine CC was obtained for 21 poorly water-soluble drugs at ambient temperature and 60 °C to calculate the aDS ratio. These aDS ratios and drug descriptors were used to train the ML models. When compared, the ANNs outperformed PLS for both sLBFCapmulMC (r2 0.90 vs. 0.56) and sLBFMaisineLC (r2 0.83 vs. 0.62), displaying smaller root mean square errors (RMSEs) and residuals upon training and testing. Across all the models, the descriptors involving reactivity and electron density were most important for prediction. This pilot study showed that ML can be employed to predict the propensity for supersaturation in LBFs, but even larger datasets need to be evaluated to draw final conclusions.

Multi-year observations of the high mountain water cycle in the Langtang catchment, Central Himalaya.

The Langtang catchment is a high mountain, third order catchment in the Gandaki basin in the Central Himalaya (28.2°N, 85.5°E), that eventually drains into the Ganges. The catchment spans an elevation range from 1400 to 7234 m a.s.l. and approximately one quarter of the area is glacierized. Numerous research projects have been conducted in the valley during the last four decades, with a strong focus on the cryospheric components of the catchment water balance. Since 2012 multiple weather stations and discharge stations provide measurements of atmospheric and hydrologic variables. Full weather stations are used to monitor at an hourly resolution all four radiation components (incoming and outgoing shortwave and longwave radiation; SWin/out and LWin/out), air temperature, humidity, wind speed and direction, and precipitation, and cover an elevational range of 3862-5330 m a.s.l. Air temperature and precipitation are monitored along elevation gradients for investigations of the spatial variability of the high mountain meteorology. Dedicated point-scale observations of snow cover, depth and water equivalent as well as ice loss have been carried out over multiple years and complement the observations of the water cycle. All data presented is openly available in a database and will be updated annually.

Glaciohydrology of the Himalaya-Karakoram.

Understanding the response of Himalayan-Karakoram (HK) rivers to climate change is crucial for ~1 billion people who partly depend on these water resources. Policy-makers tasked with sustainable water resources management require an assessment of the rivers' current status and potential future changes. We show that glacier and snow melt are important components of HK rivers, with greater hydrological importance for the Indus basin than for the Ganges and Brahmaputra basins. Total river runoff, glacier melt, and seasonality of flow are projected to increase until the 2050s, with some exceptions and large uncertainties. Critical knowledge gaps severely affect modeled contributions of different runoff components, future runoff volumes, and seasonality. Therefore, comprehensive field observation-based and remote sensing-based methods and models are needed.

Combining species specific in vitro & in silico models to predict in vivo food effect in a preclinical stage - case study of Venetoclax.

The pig has been increasingly used as a reliable preclinical model for assessing and predicting the in vivo bioavailability of different formulation strategies. Nevertheless, differences in the composition between porcine and human intestinal fluids, may impact on the solubility and dissolution behaviour of drugs, in particular BCS II/IV drugs. Recently, a porcine fasted simulated intestinal fluid (FaSSIFp) was developed to mimic the composition in the lumen of landrace pigs under fasted state conditions. In this work, we present the utilization of FaSSIFp to compare solubility against human FaSSIF & FeSSIF and further combine species specific in vitro testing with in silico predictive modelling. Venetoclax was chosen as a model drug, representing a BCS class IV drug, with a reported clinically significant positive food effect, where bioavailability is increased up to approximately five-fold when administered with a high-fat meal. Biorelevant species specific in vitro testing was a promising tool for integrating in vitro data into in silico models, using FaSSIFp resulted in reliable predictions of the plasma concentration profile in fasted pigs, based on a porcine physiologically based absorption model. The porcine physiologically based absorption model was used to prospectively simulate the impact of food on the bioavailability of venetoclax. The use of luminal solubility estimates in combination with dissolution data for venetoclax, measured in species specific simulated fluids, correctly predict the observed pig plasma concentration profile and food effect. Overall, integrating species specific in vitro - in silico models led to accurate prediction of in vivo absorption of venetoclax in a preclinical stage, which can support guidance in early decisions of drug product development. In addition, the study further demonstrated the utility of the pig model to predict the food effects of venetoclax in humans.

Exploring porcine gastric and intestinal fluids using microscopic and solubility estimates: Impact of placebo self-emulsifying drug delivery system administration to inform bio-predictive in vitro tools.

Validation and characterisation of in vitro and pre-clinical animal models to support bio-enabling formulation development is of paramount importance. In this work, post-mortem gastric and small intestinal fluids were collected in the fasted, fed state and at five sample-points post administration of a placebo Self-Emulsifying Drug Delivery System (SEDDS) in the fasted state to pigs. Cryo-TEM and Negative Stain-TEM were used for ultrastructure characterisation. Ex vivo solubility of fenofibrate was determined in the fasted-state, fed-state and post-SEDDS administration. Highest observed ex vivo drug solubility in intestinal fluids after SEDDS administration was used for optimising the biorelevant in vitro conditions to determine maximum solubility. Under microscopic evaluation, fasted, fed and SEDDS fluids resulted in different colloidal structures. Drug solubility appeared highest 1 hour post SEDDS administration, corresponding with presence of SEDDS lipid droplets. A 1:200 dispersion of SEDDS in biorelevant media matched the highest observed ex vivo solubility upon SEDDS administration. Overall, impacts of this study include increasing evidence for the pig preclinical model to mimic drug solubility in humans, observations that SEDDS administration may poorly mimic colloidal structures observed under fed state, while microscopic and solubility porcine assessments provided a framework for increasingly bio-predictive in vitro tools.

Low-Level Rifampin Resistance and rpoB Mutations in Mycobacterium tuberculosis: an Analysis of Whole-Genome Sequencing and Drug Susceptibility Test Data in New York.

Rapid and reliable detection of rifampin (RIF) resistance is critical for the diagnosis and treatment of drug-resistant and multidrug-resistant (MDR) tuberculosis. Discordant RIF phenotype/genotype susceptibility results remain a challenge due to the presence of rpoB mutations that do not confer high levels of RIF resistance, as have been exhibited in strains with mutations such as Ser450Leu. These strains, termed low-level RIF resistant, exhibit elevated RIF MICs compared to fully susceptible strains but remain phenotypically susceptible by mycobacterial growth indicator tube (MGIT) testing and have been associated with poor patient outcomes. Here, we assess RIF resistance prediction by whole-genome sequencing (WGS) among a set of 1,779 prospectively tested strains by both prevalence of rpoB gene mutation and phenotype as part of routine clinical testing during a 2.5-year period. During this time, 139 strains were found to have nonsynonymous rpoB mutations, 53 of which were associated with RIF resistance, including both low-level and high-level resistance. Resistance to RIF (1.0 µg/ml in MGIT) was identified in 43 (81.1%) isolates. The remaining 10 (18.9%) strains were susceptible by MGIT but were confirmed to be low-level RIF resistant by MIC testing. Full rpoB gene sequencing overcame the limitations of critical concentration phenotyping, probe-based genotyping, and partial gene sequencing methods. Universal clinical WGS with concurrent phenotypic testing provided a more complete understanding of the prevalence and type of rpoB mutations and their association with RIF resistance in New York.

Applying Computational Predictions of Biorelevant Solubility Ratio Upon Self-Emulsifying Lipid-Based Formulations Dispersion to Predict Dose Number.

Computational approaches are increasingly utilised in development of bio-enabling formulations, including self-emulsifying drug delivery systems (SEDDS), facilitating early indicators of success. This study investigated if in silico predictions of drug solubility gain i.e. solubility ratios (SR), after dispersion of a SEDDS in biorelevant media could be predicted from drug properties. Apparent solubility upon dispersion of two SEDDS in FaSSIF was measured for 30 structurally diverse poorly water soluble drugs. Increased drug solubility upon SEDDS dispersion was observed in all cases, with higher SRs observed for cationic and neutral versus anionic drugs at pH 6.5. Molecular descriptors and solid-state properties were used as inputs during partial least squares (PLS) modelling resulting in predictive models for SRMC (r2 = 0.81) and SRLC (r2 = 0.77). Multiple linear regression (MLR) facilitated generation of simplified SR equations with high predictivity (SRMC r2 = 0.74; SRLC r2 = 0.69), requiring only three drug properties; partition coefficient at pH 6.5 (logD6.5), melting point (Tm) and aromatic bonds as fraction of total bonds (F-AromB). Through using the equations to inform developability classification system (DCS) classes for drugs that have already been licensed as lipid-based formulations, merits for development with SEDDS was predicted for 2/3 drugs.

Pre-Clinical Evaluation of a Modified Cyclodextrin-Based Nanoparticle for Intestinal Delivery of Liraglutide.

New therapeutic approaches have been developed during recent years for the management of diabetic patients, with glucagon-like peptides analogues (GLP-1 analogues) emerging as one of the most useful therapies. However, as with human insulin analogues, translation of GLP-1 analogues into oral pharmaceutical products has been limited due to reduced oral bioavailability. Nanoparticle (NP) formulations have been investigated due to their potential to protect the drug cargo and enhance bioavailability. This study describes the pre-clinical development of a cyclodextrin-based NP formulation containing the GLP-1 analogue liraglutide for intestinal administration. A cationic amphiphilic cyclodextrin (click propyl-amine cyclodextrin (CD)) was selected as the primary complexing agent for the peptide. The resulting NPs presented an average size of 101 ± 8 nm, low polydispersity index (0.240), a negative zeta potential (-35 ± 7 mV), complete association efficiency and peptide loading of 5.0%. The optimized prototype exhibited colloidal stability in intestinal-biorelevant media up to 4 h, protecting the entrapped liraglutide from degradation by proteolytic enzymes. Intestinal administration in rats revealed effective protection and delivery of liraglutide, with a similar pharmacological response in blood glucose levels relative to subcutaneous administration of free solution. These results demonstrate the potential of the CD based formulation for further development.

Assessing Nanopore Sequencing for Clinical Diagnostics: a Comparison of Next-Generation Sequencing (NGS) Methods for Mycobacterium tuberculosis.

Next-generation sequencing technologies are being rapidly adopted as a tool of choice for diagnostic and outbreak investigation in public health laboratories. However, costs of operation and the need for specialized staff remain major hurdles for laboratories with limited resources for implementing these technologies. This project aimed to assess the feasibility of using Oxford Nanopore MinION whole-genome sequencing data of Mycobacterium tuberculosis isolates for species identification, in silico spoligotyping, detection of mutations associated with antimicrobial resistance (AMR) to accurately predict drug susceptibility profiles, and phylogenetic analysis to detect transmission between cases. The results were compared prospectively in real time to those obtained with our current clinically validated Illumina MiSeq sequencing assay for M. tuberculosis and phenotypic drug susceptibility testing results when available. Our assessment of 431 sequenced samples over a 32-week period demonstrates that, when using the proper quality controls and thresholds, the MinION can achieve levels of genotyping analysis and phenotypic resistance predictions comparable to those of the Illumina MiSeq at a very competitive cost per sample. Our results indicate that nanopore sequencing can be a suitable alternative to, or complement, currently used sequencing platforms in a clinical setting and has the potential to be widely adopted in public health laboratories in the near future.