Establishment of the Global SEND Alliance (G-SEND) in Japan and efficient creation of electronic SEND datasets between CROs.

The Standard for Exchange of Nonclinical Data (SEND), adopted by the US Food and Drug Administration (FDA), is a set of regulations for digitalization and standardization of nonclinical study data; thus, related organizations have begun implementing processes in support of SEND. The Global Editorial and Steering Committee (GESC), which provides oversight of the International Harmonization of Nomenclature and Diagnostic Criteria (INHAND), has prepared the SEND Controlled Terminology (CT) for toxicologic pathology. SEND provides electronic data standards created by the Clinical Data Interchange Standards Consortium (CDISC), and CDISC also collaborates in the implementation of SEND. Furthermore, the Pharmaceutical Users Software Exchange (PhUSE), which includes members of the US FDA, has conducted various activities to promote realistic and effective methods to implement SEND. As we reported in 2015, there is a significant variation in the efficiency and quality of SEND data implementation across pharmaceutical companies and contractors (CROs) globally. To address this problem, the Global SEND Alliance (G-SEND) was established in August 2018 to facilitate the coordination and standardization of SEND datasets across CROs in Asia. This paper reports the first method for organizationally and jointly creating consistent SEND datasets between CROs using G-SEND.

Dopamine Transporter Dynamics of N-Substituted Benztropine Analogs with Atypical Behavioral Effects.

Atypical dopamine transporter (DAT) inhibitors, despite high DAT affinity, do not produce the psychomotor stimulant and abuse profile of standard DAT inhibitors such as cocaine. Proposed contributing features for those differences include off-target actions, slow onsets of action, and ligand bias regarding DAT conformation. Several 3α-(4',4''-difluoro-diphenylmethoxy)tropanes were examined, including those with the following substitutions: N-(indole-3''-ethyl)- (GA1-69), N-(R)-2''-amino-3''-methyl-n-butyl- (GA2-50), N-2''aminoethyl- (GA2-99), and N-(cyclopropylmethyl)- (JHW013). These compounds were previously reported to have rapid onset of behavioral effects and were presently evaluated pharmacologically alone or in combination with cocaine. DAT conformational mode was assessed by substituted-cysteine accessibility and molecular dynamics (MD) simulations. As determined by substituted-cysteine alkylation, all BZT analogs except GA2-99 showed bias for a cytoplasmic-facing DAT conformation, whereas cocaine stabilized the extracellular-facing conformation. MD simulations suggested that several analog-DAT complexes formed stable R85-D476 "outer gate" bonds that close the DAT to extracellular space. GA2-99 diverged from this pattern, yet had effects similar to those of other atypical DAT inhibitors. Apparent DAT association rates of the BZT analogs in vivo were slower than that for cocaine. None of the compounds was self-administered or stimulated locomotion, and each blocked those effects of cocaine. The present findings provide more detail on ligand-induced DAT conformations and indicate that aspects of DAT conformation other than "open" versus "closed" may facilitate predictions of the actions of DAT inhibitors and may promote rational design of potential treatments for psychomotor-stimulant abuse.

Specific pathologist responses for Standard for Exchange of Nonclinical Data (SEND).

The Standard for Exchange of Nonclinical Data (SEND), introduced by the US Food and Drug Administration (FDA), is a scheme for the computerization, electronic application, and screening of preclinical data. Since its establishment, related organizations have been working together to implement SEND. However, it is difficult for individual pharmaceutical companies that often outsource to achieve complete compliance with SEND; hence, the cooperation of contract research organizations (CROs) and SEND Registered Solution Providers (RSPs) is indispensable. In SEND, most data, including those on pathology findings, are converted into controlled terminology (CT), but it is not a simple process to convert findings or levels of severity in the field of pathology, which is a descriptive science. The authors have successfully completed an FDA trial submission for a toxicology test conducted at a CRO and in doing so acquired important knowledge. This article presents a clear picture of such important knowledge from a pathologist's viewpoint.

Evaluation of Wearable Acoustic Sensors and Machine Learning Algorithms for Automated Measurement of Left Ventricular Ejection Fraction.

Left ventricular ejection fraction (EF) is a predictor of mortality and guides clinical decisions. Although transthoracic echocardiography (TTE) is commonly used for measuring EF, it has limitations, such as subjectivity and requires expert personnel. Advancements in biosensor technology and artificial intelligence are allowing systems capable of determining left ventricular function and providing automated measurement of EF. In this study, we tested new wearable automated real-time biosensors (Cardiac Performance System [CPS]) that compute EF using waveform machine learning on cardiac acoustic signals. The primary aim was to compare the accuracy of CPS EF with TTE EF. Adult patients presenting to cardiology, presurgical, and diagnostic radiology clinical settings in an academic center were enrolled. TTE examination was performed by a sonographer, followed immediately by a 3-minute recording of acoustic signals from CPS biosensors placed on the chest by nonexpert personnel. TTE EF was calculated offline using the Simpson biplane method. A total of 81 patients (aged 19 to 88 years, 27 women, 20% to 80% EF) were included. Deming regression and Bland-Altman analysis were performed to assess the accuracy of CPS EF against TTE EF. Both Deming regression (slope 0.9981; intercept 0.03415%) and Bland-Altman analysis (bias -0.0247%; limits of agreement [-11.65, 11.60]%) demonstrated equivalency between CPS EF and TTE EF. The receiver operating characteristic for measuring sensitivity and specificity of CPS in identifying subjects with abnormal EF showed an area under the curve value of 0.974 for identifying EF <35% and 0.916 for detecting EF <50% CPS EF intraoperator and interoperator assessments demonstrated low variability. In conclusion, this technology measuring cardiac function from noninvasive biosensors and machine learning on acoustic signals provides an accurate EF measurement that is automated, real-time, and acquired rapidly by personnel with minimal training.

Assessment of Left Ventricular Diastolic Function using Phonocardiogram Signals: A Comparison with Echocardiography.

This paper presents a fully-automated end-to-end phonocardiogram(PCG)-based wearable system capable of providing echocardiography-like metrics for left ventricular (LV) diastolic function assessment. Proxy metrics for five echocardiographic parameters were calculated based on physiologically-motivated features extracted from PCG signals using noise-subtraction, heartbeat-segmentation, and quality-assurance algorithms. The clinical value of these proxy metrics was evaluated using the latest American Society of Echocardiography/European Association of Cardiovascular Imaging guidelines for evaluation of LV diastolic function. When tested on a group of n=34 patients, proxy metrics successfully identified LV diastolic dysfunction in a n=29 subset with 87.5% accuracy, and elevated LV filling pressures in a n=17 subset with 75% accuracy.

Fully-Automated Diagnosis of Aortic Stenosis Using Phonocardiogram-Based Features.

The irreversible damage and eventual heart failure caused by untreated aortic stenosis (AS) can be prevented by early detection and timely intervention. Prior work in the field of phonocardiogram (PCG) signal analysis has provided proof of concept for using heart-sound data in AS diagnosis. However, such systems either require operation by trained technicians, fail to address a diverse subject set, or involve unwieldy configuration procedures that challenge real-world application. This paper presents an end-to-end, fully-automated system that uses noise-subtraction, heartbeat-segmentation and quality-assurance algorithms to extract physiologically-motivated features from PCG signals to diagnose AS. When tested on n=96 patients showing a diverse set of cardiac and non-cardiac conditions, the system was able to diagnose AS with 92% sensitivity and 95% specificity.

DARK Classics in Chemical Neuroscience: Ibogaine.

The West African iboga plant has been used for centuries by the Bwiti and Mbiri tribes to induce hallucinations during religious ceremonies. Ibogaine, the principal alkaloid responsible for iboga's psychedelic properties, was isolated and sold as an antidepressant in France for decades before its adverse effects precipitated its removal from the market. An ibogaine resurgence in the 1960s was driven by U.S. heroin addicts who claimed that ibogaine cured their opiate addictions. Behavioral pharmacologic studies in animal models provided evidence that ibogaine could blunt self-administration of not only opiates but cocaine, amphetamines, and nicotine. Ibogaine displays moderate-to-weak affinities for a wide spectrum of receptor and transporter proteins; recent work suggests that its actions at nicotinic acetylcholine receptor subtypes may underlie its reputed antiopiate effects. At micromolar levels, ibogaine is neurotoxic and cardiotoxic and has been linked to several deaths by cardiac arrest. Structure-activity studies led to the isolation of the ibogaine analog 18-methoxycoronaridine (18-MC), an α3ß4 nicotinic receptor modulator that retains ibogaine's anticraving properties with few or no adverse effects. Clinical trials of 18-MC treatment of nicotine addiction are pending. Ibogaine analogs may also hold promise for treating anxiety and depression via the "psychedelic-assisted therapy" approach that employs hallucinogens including psilocybin and methylenedioxymethamphetamine ("ecstasy").

A Role for Fragment-Based Drug Design in Developing Novel Lead Compounds for Central Nervous System Targets.

Hundreds of millions of U.S. dollars are invested in the research and development of a single drug. Lead compound development is an area ripe for new design strategies. Therapeutic lead candidates have been traditionally found using high-throughput in vitro pharmacological screening, a costly method for assaying thousands of compounds. This approach has recently been augmented by virtual screening (VS), which employs computer models of the target protein to narrow the search for possible leads. A variant of VS is fragment-based drug design (FBDD), an emerging in silico lead discovery method that introduces low-molecular weight fragments, rather than intact compounds, into the binding pocket of the receptor model. These fragments serve as starting points for "growing" the lead candidate. Current efforts in virtual FBDD within central nervous system (CNS) targets are reviewed, as is a recent rule-based optimization strategy in which new molecules are generated within a 3D receptor-binding pocket using the fragment as a scaffold. This process not only places special emphasis on creating synthesizable molecules but also exposes computational questions worth addressing. Fragment-based methods provide a viable, relatively low-cost alternative for therapeutic lead discovery and optimization that can be applied to CNS targets to augment current design strategies.