Acquisitions Driven by Stock Overvaluation

Overvaluation might drive a firm to use its stock to acquire another firm whose stock is not as overpriced. Though hypothetically desirable, these acquisitions create little, if any, value for acquirer shareholders. Two factors impede value creation for acquirer stockholders from these transactions (despite large differences in relative overvaluation at announcement): acquirers paying large premiums to targets, and investors’ correction of acquirer overvaluation during the bid period. Furthermore, acquirer CEOs obtain a large amount of new stock and option grants after acquisitions and realize a net gain in wealth, further suggesting that equity overvaluation increases agency costs and the resulting actions benefit managers more than shareholders (Jensen (2005))

Acquisitions driven by stock overvaluation: are they good deals?

Theory and recent evidence suggest that overvalued firms can create value for shareholders if they exploit their overvaluation by using their stock as currency to purchase less overvalued firms. We challenge this idea and show that, in practice, overvalued acquirers significantly overpay for their targets. These acquisitions do not, in turn, lead to synergy gains. Moreover, these acquisitions seem to be concentrated among acquirers with the largest governance problems. CEO compensation, not shareholder value creation, appears to be the main motive behind acquisitions by overvalued acquirers

Identifying Unexpected Therapeutic Targets via Chemical-Protein Interactome

Drug medications inevitably affect not only their intended protein targets but also other proteins as well. In this study we examined the hypothesis that drugs that share the same therapeutic effect also share a common therapeutic mechanism by targeting not only known drug targets, but also by interacting unexpectedly on the same cryptic targets. By constructing and mining an Alzheimer's disease (AD) drug-oriented chemical-protein interactome (CPI) using a matrix of 10 drug molecules known to treat AD towards 401 human protein pockets, we found that such cryptic targets exist. We recovered from CPI the only validated therapeutic target of AD, acetylcholinesterase (ACHE), and highlighted several other putative targets. For example, we discovered that estrogen receptor (ER) and histone deacetylase (HDAC), which have recently been identified as two new therapeutic targets of AD, might already have been targeted by the marketed AD drugs. We further established that the CPI profile of a drug can reflect its interacting character towards multi-protein sets, and that drugs with the same therapeutic attribute will share a similar interacting profile. These findings indicate that the CPI could represent the landscape of chemical-protein interactions and uncover “behind-the-scenes” aspects of the therapeutic mechanisms of existing drugs, providing testable hypotheses of the key nodes for network pharmacology or brand new drug targets for one-target pharmacology paradigm

DDI-CPI, a server that predicts drug–drug interactions through implementing the chemical–protein interactome

Drug–drug interactions (DDIs) may cause serious side-effects that draw great attention from both academia and industry. Since some DDIs are mediated by unexpected drug–human protein interactions, it is reasonable to analyze the chemical–protein interactome (CPI) profiles of the drugs to predict their DDIs. Here we introduce the DDI-CPI server, which can make real-time DDI predictions based only on molecular structure. When the user submits a molecule, the server will dock user's molecule across 611 human proteins, generating a CPI profile that can be used as a feature vector for the pre-constructed prediction model. It can suggest potential DDIs between the user's molecule and our library of 2515 drug molecules. In cross-validation and independent validation, the server achieved an AUC greater than 0.85. Additionally, by investigating the CPI profiles of predicted DDI, users can explore the PK/PD proteins that might be involved in a particular DDI. A 3D visualization of the drug-protein interaction will be provided as well. The DDI-CPI is freely accessible at http://cpi.bio-x.cn/ddi/

Effectiveness of a Multidisciplinary Rehabilitation Program Following Shoulder Injury

Background: Shoulder injuries in working age adults result in a major cost to the health care system. The purpose of this study was to examine the effectiveness of a new multidisciplinary rehabilitation program and to explore factors that affected a successful return to work (RTW) in injured workers with shoulder problems who received this program. Methods: This was a prospective longitudinal study. The patient-oriented outcome measures were the Numeric Pain Rating Scale (NPRS) and the Disabilities of the Arm, Shoulder, and Hand (DASH). Range of motion (ROM) in flexion, abduction, and external rotation and strength in lifting and push/pull were documented. All outcomes were measured before and at the completion of the program. Results: Data of 68 patients were used for analysis. All outcomes showed a statistically significant improvement over time. Conclusions: Multidisciplinary rehabilitation programs help to improve pain, disability, ROM, strength, and facilitate RTW. Higher stress and a fast-paced work environment increased the risk of not progressing in work status

DRAR-CPI: a server for identifying drug repositioning potential and adverse drug reactions via the chemical–protein interactome

Identifying new indications for existing drugs (drug repositioning) is an efficient way of maximizing their potential. Adverse drug reaction (ADR) is one of the leading causes of death among hospitalized patients. As both new indications and ADRs are caused by unexpected chemical–protein interactions on off-targets, it is reasonable to predict these interactions by mining the chemical–protein interactome (CPI). Making such predictions has recently been facilitated by a web server named DRAR-CPI. This server has a representative collection of drug molecules and targetable human proteins built up from our work in drug repositioning and ADR. When a user submits a molecule, the server will give the positive or negative association scores between the user’s molecule and our library drugs based on their interaction profiles towards the targets. Users can thus predict the indications or ADRs of their molecule based on the association scores towards our library drugs. We have matched our predictions of drug–drug associations with those predicted via gene-expression profiles, achieving a matching rate as high as 74%. We have also successfully predicted the connections between anti-psychotics and anti-infectives, indicating the underlying relevance of anti-psychotics in the potential treatment of infections, vice versa. This server is freely available at http://cpi.bio-x.cn/drar/

Exploring Off-Targets and Off-Systems for Adverse Drug Reactions via Chemical-Protein Interactome — Clozapine-Induced Agranulocytosis as a Case Study

In the era of personalized medical practice, understanding the genetic basis of patient-specific adverse drug reaction (ADR) is a major challenge. Clozapine provides effective treatments for schizophrenia but its usage is limited because of life-threatening agranulocytosis. A recent high impact study showed the necessity of moving clozapine to a first line drug, thus identifying the biomarkers for drug-induced agranulocytosis has become important. Here we report a methodology termed as antithesis chemical-protein interactome (CPI), which utilizes the docking method to mimic the differences in the drug-protein interactions across a panel of human proteins. Using this method, we identified HSPA1A, a known susceptibility gene for CIA, to be the off-target of clozapine. Furthermore, the mRNA expression of HSPA1A-related genes (off-target associated systems) was also found to be differentially expressed in clozapine treated leukemia cell line. Apart from identifying the CIA causal genes we identified several novel candidate genes which could be responsible for agranulocytosis. Proteins related to reactive oxygen clearance system, such as oxidoreductases and glutathione metabolite enzymes, were significantly enriched in the antithesis CPI. This methodology conducted a multi-dimensional analysis of drugs' perturbation to the biological system, investigating both the off-targets and the associated off-systems to explore the molecular basis of an adverse event or the new uses for old drugs

Measurement of neutron-proton capture in the SNO+ water phase

The SNO+ experiment collected data as a low-threshold water Cherenkov detector from September 2017 to July 2019. Measurements of the 2.2-MeV $\gamma$ produced by neutron capture on hydrogen have been made using an Am-Be calibration source, for which a large fraction of emitted neutrons are produced simultaneously with a 4.4-MeV $\gamma$. Analysis of the delayed coincidence between the 4.4-MeV $\gamma$ and the 2.2-MeV capture $\gamma$ revealed a neutron detection efficiency that is centered around 50% and varies at the level of 1% across the inner region of the detector, which to our knowledge is the highest efficiency achieved among pure water Cherenkov detectors. In addition, the neutron capture time constant was measured and converted to a thermal neutron-proton capture cross section of $336.3^{+1.2}_{-1.5}$ mb