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Anton Pozniak

Researcher at Imperial College London

Publications -  270
Citations -  13961

Anton Pozniak is an academic researcher from Imperial College London. The author has contributed to research in topics: Ritonavir & Acquired immunodeficiency syndrome (AIDS). The author has an hindex of 53, co-authored 241 publications receiving 13079 citations. Previous affiliations of Anton Pozniak include Oxleas NHS Foundation Trust & Bristol-Myers Squibb.

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Journal ArticleDOI

HIV treatment cascades: how can all countries reach the UNAIDS 90-90-90 target?

Andrew F. Hill, +1 more
- 28 Nov 2015 - 
TL;DR: It is likely that at least 35 million people would need to be on treatment by 2020 to include newly infected people in the 90-90-90 targets.
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Prioritising the most needed paediatric antiretroviral formulations: the PADO4 list

TL;DR: The Paediatric Antiretroviral Drug Optimization group reviews medium-term and long-term priorities for antireTroviral drug development to guide industry and other stakeholders on formulations most needed for low-income and middle-income countries.
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The SPICE study: 48-week activity of combinations of saquinavir soft gelatin and nelfinavir with and without nucleoside analogues. Study of Protease Inhibitor Combinations in Europe.

TL;DR: Quadruple therapy gave a more durable response than triple therapy with either single protease inhibitor and might particularly benefit NRTI-experienced patients and those with high baseline viral loads.
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Sequential Population Pharmacokinetic Modeling of Lopinavir and Ritonavir in Healthy Volunteers and Assessment of Different Dosing Strategies

TL;DR: Nonlinear mixed-effects modeling allows a better understanding of the interaction between lopinavir and ritonavir and may allow a better prediction of lopvinir concentrations and assessments of different dosing strategies.
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The development of an expert system to predict virological response to HIV therapy as part of an online treatment support tool.

TL;DR: The development and testing of random forest models to power an online treatment selection tool and achieved a consistent, high level of accuracy in predicting treatment responses, which was markedly superior to that of genotypic sensitivity scores.