Macrophage migration inhibitory factor (MIF) has emerged as a promising drug target in diseases including sepsis, rheumatoid arthritis, and cancer. MIF has multiple properties that favor development of specific, targeted therapies: it is expressed broadly among human cells, has noted roles in diverse inflammatory and oncological processes, and has intrinsic enzymatic activity amenable to high-throughput screening. Despite these advantages, anti-MIF therapy remains well behind other cytokine-targeted therapeutics, with no small molecules in the pipeline for clinical development and anti-MIF antibodies only recently beginning clinical trials. Areas covered: In this review we summarize current literature regarding MIF structure and function-including challenges and controversies that have arisen in studies of anti-MIF therapeutics-and propose a strategy for development of clinically relevant anti-MIF drugs. Expert opinion: We believe that the field of anti-MIF therapeutics would benefit from capitalizing on the protein's multiple assets while acknowledging their flaws. The tautomerase enzymatic site of MIF may not be active biologically, but can nonetheless offer a high-throughput method to highlight molecules of interest that can affect its other, frequently intertwined bioactivities. Future work should also focus on developing more robust assays for MIF bioactivity that can be used for second-pass screening and specificity studies.

Edited by Dennis VoelkerMacrophage migration inhibitory factor (MIF) is a pleiotropic cytokine that has been implicated in a broad range of inflammatory and oncologic diseases. MIF is unique among cytokines in terms of its release profile and inflammatory role, notably as an endogenous counter-regulator of the anti-inflammatory effects of glucocorticoids. In addition, it exhibits a catalytic tautomerase activity amenable to the design of high affinity small molecule inhibitors. Although several classes of these compounds have been identified, biologic characterization of these molecules remains a topic of active investigation. In this study, we used in vitro LPS-driven assays to characterize representative molecules from several classes of MIF inhibitors. We determined that MIF inhibitors exhibit distinct profiles of anti-inflammatory activity, especially with regard to TNF␣. We further investigated a molecule with relatively low anti-inflammatory activity, compound T-614 (also known as the anti-rheumatic drug iguratimod), and found that, in addition to exhibiting selective MIF inhibition in vitro and in vivo, iguratimod also has additive effects with glucocorticoids. Furthermore, we found that iguratimod synergizes with glucocorticoids in attenuating experimental autoimmune encephalitis, a model of multiple sclerosis. Our work identifies iguratimod as a valuable new candidate for drug repurposing to MIF-relevant diseases, including multiple sclerosis.

Although major depressive disorder imposes a serious public health burden and affects nearly one in six individuals in developed countries over their lifetimes, there is still no consensus on its pathophysiology. Inflammation and cytokines have emerged as a promising new avenue in depression research, and, in particular, macrophage migration inhibitory factor (MIF) has been shown to be significant in depression physiology. In this review we summarize current research on MIF and depression. We highlight the arguments for MIF as a pro- and antidepressant species and discuss the potential implications for therapeutics.

Short-period cataclysmic variables (spCVs), with orbital periods below the period gap (P orb < 2 hr), offer insight into the evolutionary models of CVs and can serve as strong emitters of detectable gravitational waves (GWs) for next-generation space-based GW observatories. To identify new spCV candidates, we crossmatch a catalog of known CVs with periods from 70 min to 8 hr to sources with well-measured parallaxes in the Gaia second data release (DR2). We uncover and fit a surprisingly (apparently) monotonic relationship between the color-absolute-magnitude diagram (CMD) position and P orb of these CVs, revealed in DR2. To supplement the CMD-P orb relation we also develop a method for identifying sources with large photometric variability, a characteristic trait of spCVs. Though Gaia is inherently a time-domain survey, the DR2 contains only a small fraction of sources with photometric light curves. Using such light curves, however, we construct a machine-learned regression model to predict physically informative variability metrics for sources in the CMD locus of known spCVs based solely on time-averaged observational covariates present in DR2. Using this approach we identify 3,253 candidate spCVs, of which ∼ 95% are previously unknown. Inspection of archival SDSS spectra of these candidates suggests that >82% are likely to be spCVs. This is a noticeably higher recovery rate than the typical recovery (∼30%) in previous light-curve searches, which bias toward flaring and active systems. We obtain optical spectra of nine previously uncharacterized systems with the Shane telescope at Lick Observatory and confirm that all objects are CV systems. We measure P orb for seven systems using archival Gaia and Palomar Transient Factory light curves, all of which are spCVs and three of which do not have previous P orb measurements. We use the CMD-P orb relation to infer the detectability of these systems to the upcoming LISA mission, and find that six of them may be coherent LISA verification binaries, with an estimated SNR > 5 in the 4 yr mission. This paper demonstrates that the time-averaged Gaia catalog is a powerful tool in the methodical discovery and characterization of even semi-stochastic time-varying objects, making it complementary to missions like ZTF, TESS, and the Vera Rubin LSST in the efficient search for rare and unusual variable systems.