Current State and Opportunities with Long-acting Injectables: Industry Perspectives from the Innovation and Quality Consortium "Long-Acting Injectables" Working Group.

Long-acting injectable (LAI) formulations can provide several advantages over the more traditional oral formulation as drug product opportunities. LAI formulations can achieve sustained drug release for extended periods of time, which results in less frequent dosing requirements leading to higher patient adherence and more optimal therapeutic outcomes. This review article will provide an industry perspective on the development and associated challenges of long-acting injectable formulations. The LAIs described herein include polymer-based formulations, oil-based formulations, and crystalline drug suspensions. The review discusses manufacturing processes, including quality controls, considerations of the Active Pharmaceutical Ingredient (API), biopharmaceutical properties and clinical requirements pertaining to LAI technology selection, and characterization of LAIs through in vitro, in vivo and in silico approaches. Lastly, the article includes a discussion around the current lack of suitable compendial and biorelevant in vitro models for the evaluation of LAIs and its subsequent impact on LAI product development and approval.

Long-term time-series pollution forecast using statistical and deep learning methods.

Tackling air pollution has become of utmost importance since the last few decades. Different statistical as well as deep learning methods have been proposed till now, but seldom those have been used to forecast future long-term pollution trends. Forecasting long-term pollution trends into the future is highly important for government bodies around the globe as they help in the framing of efficient environmental policies. This paper presents a comparative study of various statistical and deep learning methods to forecast long-term pollution trends for the two most important categories of particulate matter (PM) which are PM2.5 and PM10. The study is based on Kolkata, a major city on the eastern side of India. The historical pollution data collected from government set-up monitoring stations in Kolkata are used to analyse the underlying patterns with the help of various time-series analysis techniques, which is then used to produce a forecast for the next two years using different statistical and deep learning methods. The findings reflect that statistical methods such as auto-regressive (AR), seasonal auto-regressive integrated moving average (SARIMA) and Holt-Winters outperform deep learning methods such as stacked, bi-directional, auto-encoder and convolution long short-term memory networks based on the limited data available.

Effect of bovine serum albumin on drug permeability estimation across Caco-2 monolayers.

The purpose of this study was to explore approaches to more accurately assess Caco-2 permeability of poorly water-soluble new chemical entities (NCEs) in an effort to determine their biopharmaceutics classification system (BCS) permeability class with a higher level of confidence. The transport of reference compounds and NCEs (Sch-Y, Sch 56592) was studied across Caco-2 monolayers in the absence or presence of varying percentage of bovine serum albumin (BSA) in the receiver chamber. The inclusion of 0.5-4% BSA in the receiver chamber caused a 4-5-fold increase in Sch-Y P(app), while Sch 56592 P(app) was not significantly influenced. Amongst reference solutes, the P(app) ratio (+BSA/ctrl) was significant (1.3-fold) only for diltiazem (log PC=2.7, plasma protein binding=78%), but the prediction of human oral absorption for such drugs was not affected by the presence of BSA in receiver. In summary, the use of 4% BSA in the receiver chamber during transport studies can dramatically affect the estimated Caco-2 P(app) and BCS permeability ranking of highly lipophilic NCEs, as in the case of Sch-Y with a log PC of 4.0. For Sch-Y, this is presumably due to improved sink conditions and/or a reduction in non-specific drug adsorption to plastic wells. In contrast, the permeability classification of Sch 56592 (log PC=2.4) based on estimated Caco-2 P(app) values is not affected by the presence of receiver BSA.