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Record W2994967047 · doi:10.1111/apt.15581

Review article: clinical pharmacology of current and investigational hepatitis B virus therapies

2019· review· en· W2994967047 on OpenAlexaff
Elise J. Smolders, David M. Burger, Jordan J. Feld, Jennifer J. Kiser

Bibliographic record

VenueAlimentary Pharmacology & Therapeutics · 2019
Typereview
Languageen
FieldMedicine
TopicHepatitis B Virus Studies
Canadian institutionsToronto Liver CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineHBsAgHepatitis B virusHepatitis BPharmacokineticsDrugInternal medicineClinical trialPharmacologyVirusVirology

Abstract

fetched live from OpenAlex

BACKGROUND: Treatment of hepatitis B virus (HBV) infection with current therapy suppresses HBV DNA, but loss of hepatitis B surface antigen (HBsAg; functional cure), is rare. Multiple compounds are under investigation. AIMS: To describe the pharmacology, including drug interactions, efficacy, safety and mechanisms of action of investigational compounds for HBV infection. METHODS: Descriptive review using PubMed and Google to identify literature/conference papers on investigational compounds (≥Phase 2) with data on efficacy and safety in HBV-infected patients. RESULTS: UI/mL) HBsAg in 85% of patients (12 weeks treatment). There are some safety concerns with investigational agents (e.g., increased bile acids with bulevirtide, and liver enzyme flares with REP-2139) which will require a risk benefit assessment compared with current therapies. Single and multidose pharmacokinetic data are available for bulevirtide, JNJ-56136379, ABI-H0731; no such data are available for REP-2139, ARO-HBV, inarigivir. Initial drug interaction assessments have been performed with bulevirtide and inarigivir (only in vitro). CONCLUSIONS: There are promising investigational therapies for HBV infection. Increasing the potential for HBsAg loss may result in more patients achieving functional cure. However, many knowledge gaps remain such as pharmacokinetics in those with HBV, cirrhosis and renal impairment but also the interaction potential between investigational therapies, risk-benefit profiles, and potential for drug interactions with medications used to treat comorbidities associated with aging.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.852
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.246
GPT teacher head0.499
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations41
Published2019
Admission routes1
Has abstractyes

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