The influenza A virus contains a proton-selective ion channel (M2) that is the target of the adamantane family of drug inhibitors. Two recently published studies relating to adamantane binding of the M2 ion channel using X-ray crystallography and solution NMR have reignited interest in the potential use of adamantanes in combating the spread of influenza A. However, these two studies propose different binding sites for the adamantane drugs with the X-ray M2/ amantadine structure favoring an ion channel pore-binding model and the solution NMR M2/rimantadine structure suggesting the existence of a lipid-facing binding pocket. We conducted a series of surface plasmon resonance (SPR) experiments designed to accurately measure the affinity of amantadine and rimantadine to M2 ion channels embedded in 1,2-dimyristoyl-sn-glycero-phosphocholine (DMPC) liposomes. We find that this class of drug is capable of binding M2 with two different affinities in the order of 10 −4 and 10 −7 M, suggesting that both proposed binding sites are feasible. Furthermore, by examining drug binding to M2 mutant constructs (V27A, S31N, and D44A), it was possible to probe the location of the two binding sites. We show that a high-affinity binding site corresponds to the M2 ion channel pore whereas the secondary, low-affinity binding site can be attributed to the lipid face of the pore. These SPR results are in excellent agreement with the most recent solid-state NMR study of amantadinebound M2 in lipid bilayers and provide independent support that the ion channel pore-binding site is responsible for the pharmacological activity elicited by the adamantane drugs.adamantane drug resistance | surface plasmon resonance | viral ion channel | mutational analysis A s evidenced by recent H5N1 (avian) and H1N1 (swine) influenza outbreaks, the influenza A virus poses an ever-present global threat to human health. One of the initial responses in combating this disease involves the use of antiviral drugs such as the neuraminidase inhibitors oseltamivir and zanamivir. Whereas the efficacy of these drugs is well established, another class of approved antiviral drug known as adamantane M2 inhibitors (amantadine and rimantadine) has in the past been shown to be effective in treating seasonal influenza (1). Unfortunately, the emergence of several adamantane-resistant M2 mutations has severely curtailed the effectiveness of this class of drug to the extent that the Centers for Disease Control have recommended discontinuing its use (2). However, the recent flu outbreaks have demonstrably highlighted the immense benefit of having an effective antiviral treatment at hand, thereby reigniting the search for novel M2 inhibitors (3, 4).The role of the influenza A M2 protein has been well documented. It forms a proton ion channel that operates early in the viral life cycle by facilitating the acidification of the endosomally entrapped virus, thereby enabling release of its RNA genome to allow viral replication (5). The M2 ion channel is 97 amino acids in size but it is t...

No abstract

Viral ion channels or viroporins are short membrane proteins that participate in wide-ranging functions including virus replication and entry, assembly, and virus release. One such viroporin is the 81 amino acid residue Vpu protein derived from HIV-1. This protein consists of one transmembrane (TM) and two cytoplasmic helical domains, the former of which oligomerises to form cation-selective ion channels. In this study, we investigate the binding properties of amiloride compounds to Vpu embedded into liposomes using surface plasmon resonance (SPR). We explore the Vpu ion channel inhibitor, hexamethylene amiloride (HMA), as a molecular tool to examine the potential interactive role of key TM residues, Trp23, Ser24, and Glu29, in terms of positioning of these residues on the channel pore and the orientation of its constituent helices. The study provides experimental support that a direct interaction between Ser24 and HMA occurs and that this residue is most likely located in the channel pore. Mutation of Trp23 does not impact HMA affinity suggesting no direct involvement in binding and that this residue is lipid facing. These findings indicate that small molecules such as amilorides are capable of specifically interacting with Vpu ion channels. Although a correlation between ion channel and functional activity cannot be dismissed, alternative mechanisms involving protein-protein interactions may play an important role in the efficacy of these compounds.

Purpose Prior literature reviews have identified gaps in understanding of how postmarketing safety labeling changes and related FDA communications impact key clinical and behavioral outcomes. We conducted a review of newly published studies on this topic to determine what new evidence exists and to identify which gaps may still remain. We believe that this information can support FDA as it develops and implements future risk communication approaches. Methods We searched PubMed and Embase for studies published between January 1, 2010, and August 7, 2017 that examined the impact of labeling changes or associated FDA safety‐related communications. For each study, we extracted information on research design and findings for key clinical outcomes and behaviors. We also conducted a ROBINS‐I review to identify potential for bias in the research design of each study. Results We found that the estimated impacts of FDA labeling changes on several key outcomes—including adverse events—varied. Labeling changes also yielded unintended consequences on drug prescribing in some cases, despite low provider adherence. Finally, some studies we reviewed exhibited potential for bias due to confounding, among other factors. Conclusions The new studies we reviewed contain many of the same limitations identified in previously published reviews. While there are several challenges to conducting this research there is substantial room for improvement in the quality of the evidence base. More information, particularly with respect to the types of populations and medications affected by labeling changes, is needed to support the development of more effective and targeted safety communications.

Generic drug products are approved by the US Food and Drug Administration (FDA) through Abbreviated New Drug Applications (ANDAs). The ANDA review and approval involves multiple offices across the FDA. Forecasting ANDA submissions can critically inform resource allocation and workload management. In this work, we used machine learning (ML) methodologies to predict the time to first ANDA submissions referencing new chemical entities following their earliest lawful ANDA submission dates. Drug product information, regulatory factors, and pharmacoeconomic factors were used as modeling inputs. The random survival forest ML method, as well as the conventional Cox model, was used for ANDA submission predictions. The ML method outperformed the conventional Cox regression model in predictive performance that was adequately assessed by both internal and external validations. In conclusion, it can potentially serve as an effective forecasting tool for strategic workload and research planning for generic applications.Facilitating increased competition in the market for prescription drugs through the approval of lower-cost and high-quality generic medicines is one of the missions for the US Food and Drug Administration (FDA). 1 A generic drug product is therapeutically equivalent to the referenced brand-name drug product, and thus can serve as a safe, effective, lower-cost alternative to its (ANDA) submissions referencing new chemical entities (NCEs) following their earliest lawful ANDA submission dates is of high importance for the timely development of product specific guidance for generic firms and strategic planning to conduct regulatory research to close scientific gaps. Machine learning (ML) methodologies have been recognized as powerful tools for their predictive performances and have been employed in many different fields. The random survival forest (RSF) method was used to learn and predict first-time ANDA submissions. The developed RSF method, as validated by internal and external datasets, showed superior performance as opposed to the conventional Cox regression model in predictive performance. This effort also shows that ML can serve as an effective forecasting tool for strategic workload and research planning. WHAT QUESTION DID THIS STUDY ADDRESS?  Is it feasible to employ the random survival forest (RSF) ML method to forecast the time to first ANDA submissions referencing new chemical entities (NCE) following their earliest lawful ANDA submission dates? WHAT DOES THIS STUDY ADD TO OUR KNOW-LEDGE? By leveraging drug product, regulatory, and pharmacoeconomic information, the proposed time-to-event model by RSF can well predict the time to first ANDA submission referencing a NCE drug product and identify the influential factors driving a submission. HOW MIGHT THIS CHANGE CLINICAL PHARMA-COLOGY OR TRANSLATIONAL SCIENCE?  ML-based time-to-event methodologies can be effective toolsets used for strategic workload and research planning in the regulatory setting and can outperform the conventional approaches, such as ...