Localized Immunotherapy via Liposome-Anchored Anti-CD137 + IL-2 Prevents Lethal Toxicity and Elicits Local and Systemic Antitumor Immunity

Immunostimulatory agonists such as anti-CD137 and interleukin (IL)-2 have elicited potent antitumor immune responses in preclinical studies, but their clinical use is limited by inflammatory toxicities that result upon systemic administration. We hypothesized that by rigorously restricting the biodistribution of immunotherapeutic agents to a locally accessible lesion and draining lymph node(s), effective local and systemic antitumor immunity could be achieved in the absence of systemic toxicity. We anchored anti-CD137 and an engineered IL-2Fc fusion protein to the surfaces of PEGylated liposomes, whose physical size permitted dissemination in the tumor parenchyma and tumor-draining lymph nodes but blocked entry into the systemic circulation following intratumoral injection. In the B16F10 melanoma model, intratumoral liposome-coupled anti-CD137 + IL-2Fc therapy cured a majority of established primary tumors while avoiding the lethal inflammatory toxicities caused by equivalent intratumoral doses of soluble immunotherapy. Immunoliposome therapy induced protective antitumor memory and elicited systemic antitumor immunity that significantly inhibited the growth of simultaneously established distal tumors. Tumor inhibition was CD8[superscript +] T-cell–dependent and was associated with increased CD8[superscript +] T-cell infiltration in both treated and distal tumors, enhanced activation of tumor antigen–specific T cells in draining lymph nodes, and a reduction in regulatory T cells in treated tumors. These data suggest that local nanoparticle-anchored delivery of immuno-agonists represents a promising strategy to improve the therapeutic window and clinical applicability of highly potent but otherwise intolerable regimens of cancer immunotherapy.Dana-Farber/Harvard Cancer Center-MIT Bridge Project Fun

Distribution of manganese and other biometals in flatiron mice

Flatiron (ffe) mice display features of “ferroportin disease” or Type IV hereditary hemochromatosis. While it is known that both Fe and Mn metabolism are impaired in flatiron mice, the effects of ferroportin (Fpn) deficiency on physiological distribution of these and other biometals is unknown. We hypothesized that Fe, Mn, Zn and/or Cu distribution would be altered in ffe/+ compared to wild-type (+/+) mice. ICP-MS analysis showed that Mn, Zn and Cu levels were significantly reduced in femurs from ffe/+ mice. Bone deposits reflect metal accumulation, therefore these data indicate that Mn, Zn and Cu metabolism are affected by Fpn deficiency. The observations that muscle Cu, lung Mn, and kidney Cu and Zn levels were reduced in ffe/+ mice support the idea that metal metabolism is impaired. While all four biometals appeared to accumulate in brains of flatiron mice, significant gender effects were observed for Mn and Zn levels in male ffe/+ mice. Metals were higher in olfactory bulbs of ffe/+ mice regardless of gender. To further study brain metal distribution, 54MnCl2 was administered by intravenous injection and total brain 54Mn was measured over time. At 72 h, 54Mn was significantly greater in brains of ffe/+ mice compared to +/+ mice while blood 54Mn was cleared to the same levels by 24 h. Taken together, these results indicate that Fpn deficiency decreases Mn trafficking out of the brain, alters body Fe, Mn, Zn and Cu levels, and promotes metal accumulation in olfactory bulbs. Electronic supplementary material The online version of this article (doi:10.1007/s10534-015-9904-2) contains supplementary material, which is available to authorized users

Vaccine delivery with microneedle skin patches in nonhuman primates

Transcutaneous drug delivery from planar skin patches is effective for small-molecule drugs and skin-permeable vaccine adjuvants. However, to achieve efficient delivery of vaccines and other macromolecular therapeutics into the skin, penetration of the stratum corneum is needed. Topically applied skin patches with micron-scale projections ('microneedles') pierce the upper layers of the skin and enable vaccines that are coated on or encapsulated within the microneedles to be dispersed into the skin. Although millimeter-scale syringes have shown promise for vaccine delivery in humans and technologies, such as the Dermaroller (Dermaroller, Wolfenbüttel, Germany), exist for creating microscale punctures in the skin for delivery of solutions of therapeutics, solid microprojection microneedles coated with dry vaccine formulations offer a number of valuable features for vaccination, including reduced risk of blood-borne pathogen transmission or needle-stick injury, the potential for vaccine administration by minimally trained personnel or even self administration and the use of solid-state vaccine formulations that may reduce or eliminate cold-chain requirements in vaccine distribution. Recent studies in mice have demonstrated the ability of microneedles to effectively deliver vaccines to the skin, eliciting protective immunity to influenza, hepatitis C and West Nile virus.Ragon Institute of MGH, MIT and HarvardMassachusetts Institute of TechnologyHarvard UniversityNational Institutes of Health (U.S.) (AI095109)National Institutes of Health (U.S.) (AI096040)National Institutes of Health (U.S.) (AI095985)National Institutes of Health (U.S.) (AI078526)National Institutes of Health (U.S.) (AI060354)United States. Dept. of Defense (Contract W911NF-07-D-0004

A machine learning and network framework to discover new indications for small molecules.

Drug repurposing, identifying novel indications for drugs, bypasses common drug development pitfalls to ultimately deliver therapies to patients faster. However, most repurposing discoveries have been led by anecdotal observations (e.g. Viagra) or experimental-based repurposing screens, which are costly, time-consuming, and imprecise. Recently, more systematic computational approaches have been proposed, however these rely on utilizing the information from the diseases a drug is already approved to treat. This inherently limits the algorithms, making them unusable for investigational molecules. Here, we present a computational approach to drug repurposing, CATNIP, that requires only biological and chemical information of a molecule. CATNIP is trained with 2,576 diverse small molecules and uses 16 different drug similarity features, such as structural, target, or pathway based similarity. This model obtains significant predictive power (AUC = 0.841). Using our model, we created a repurposing network to identify broad scale repurposing opportunities between drug types. By exploiting this network, we identified literature-supported repurposing candidates, such as the use of systemic hormonal preparations for the treatment of respiratory illnesses. Furthermore, we demonstrated that we can use our approach to identify novel uses for defined drug classes. We found that adrenergic uptake inhibitors, specifically amitriptyline and trimipramine, could be potential therapies for Parkinson's disease. Additionally, using CATNIP, we predicted the kinase inhibitor, vandetanib, as a possible treatment for Type 2 Diabetes. Overall, this systematic approach to drug repurposing lays the groundwork to streamline future drug development efforts

A machine learning and network framework to discover new indications for small molecules.

Drug repurposing, identifying novel indications for drugs, bypasses common drug development pitfalls to ultimately deliver therapies to patients faster. However, most repurposing discoveries have been led by anecdotal observations (e.g. Viagra) or experimental-based repurposing screens, which are costly, time-consuming, and imprecise. Recently, more systematic computational approaches have been proposed, however these rely on utilizing the information from the diseases a drug is already approved to treat. This inherently limits the algorithms, making them unusable for investigational molecules. Here, we present a computational approach to drug repurposing, CATNIP, that requires only biological and chemical information of a molecule. CATNIP is trained with 2,576 diverse small molecules and uses 16 different drug similarity features, such as structural, target, or pathway based similarity. This model obtains significant predictive power (AUC = 0.841). Using our model, we created a repurposing network to identify broad scale repurposing opportunities between drug types. By exploiting this network, we identified literature-supported repurposing candidates, such as the use of systemic hormonal preparations for the treatment of respiratory illnesses. Furthermore, we demonstrated that we can use our approach to identify novel uses for defined drug classes. We found that adrenergic uptake inhibitors, specifically amitriptyline and trimipramine, could be potential therapies for Parkinson's disease. Additionally, using CATNIP, we predicted the kinase inhibitor, vandetanib, as a possible treatment for Type 2 Diabetes. Overall, this systematic approach to drug repurposing lays the groundwork to streamline future drug development efforts