Lung cancer is the leading cause of cancer related deaths in the US with approximately 150,000 deaths in 2017. Currently, mildly ineffective chemotherapeutics ripe with adverse side effects are the standard treatment. Antisense oligonucleotides (ASOs) are able to selectively modulate expression of target genes by preventing mRNA translation into protein. ASOs already have been shown to have promising efficacy in preclinical studies. Akt-1 and Bcl2 are considered significant potential targets for inhibition via ASOs due to their roles in various cancer pathways. Under current production methods, ASOs face in vivo challenges like nuclease degradation, low target binding affinity, low membrane permeability, and off-target effects.
Researchers at The Ohio State University, led by Dr. Robert Lee of the College of Pharmacy, have solved a number of these problems through their innovative use of lipid nanoparticles, gapmer design strategy through the addition of 2'-O-methyl modifications, and T7 peptide for tumor targeting. The use of the lipid nanoparticle allows for improved durability of the ASOs and increased membrane permeability. The gapmer design via the 2'-OMe modification protect the ASOs from nucleases thus mitigating the risk for degradation. The T7 peptide conjugation with the nanoparticle provides improved targeting of and uptake into tumor cell thus also decreasing off target effects. These results were confirmed both in vivo and in vitro. The triumvirate of ASO modification, nanoparticle encapsulation, and peptide conjugation provides adequate solutions to a number of the problems plaguing ASO cancer treatments. In addition, nanoparticle-based co-delivery of a synergistic ratio of two oligos against separate targets, bcl-2 and akt-1, lead to increased therapeutic efficacy.
Stimuli-responsive nanoparticles (NPs) hold great promise for controlling drug release inside cancer cells. An obstacle to the effectiveness of this approach to chemotherapeutic drug delivery is the rapid expulsion of the delivered drug catalyzed by efflux pumps in the cell's plasma membrane. Multidrug-resistant cells, such as cancer stem cells (CSCs), are characterized by overexpression of these transporter-based efflux pumps. While stimuli-responsive NPs may be taken up by cancer cells via endocytosis, most of these NPs, such as pH- or heat-responsive ones, slowly release their drug cargo over hours to days resulting in the slowly released drug being pumped out of the cells before binding with the drug target within the cell nucleus. This creates a need for for a rapid-release NP such that the quickly-released drug can bind with its target before efflux pump depletion.
Researchers at Ohio State University and the University of Maryland have developed a hybrid (phospholipid/polymer) nanoparticle that achieves burst-release of the encapsulated drug by quick, irreversible disassembly of the NP. This NP disassembly occurs with ice cooling, or via other cooling procedures, below about 12°C. This cooling also, of itself, compromises the pumping capacity of the membrane transporters. Under the cooled condition, binding of the drug, for example DOX, with its target within the cell nucleus is evident, while it is minimal in cells kept at 37°C. NP targeting of cancer cells is achieved by decorating the NP surface with hyaluronic acid because hyaluronic acid is the natural ligand of the variant CD44 commonly overexpressed on many types of cancer cells and particularly CSCs. The researchers have demonstrated effective killing of drug-resistant ovarian cancer cells and associated CSCs in vitro and have also demonstrated destruction of both subcutaneous and orthotopic ovarian tumors in vivo with no evident systemic toxicity.
Although acoustic beamforming systems that are based on phase delays can be used to enhance directional and spectral sensitivities, each source or receiver must be individually controlled by appropriate phase delays to guide the acoustic energy radiation/reception sensitivities. This results in a massive computational burden in order to realize intense confinement of acoustic waves in angular regions or to achieve focusing at specific spatial locations, particularly at high frequencies. The spatial distribution in such systems may also result in physically large platforms. There is a need for a wave-energy guiding system with high sensitivity, reduced implementation complexity, substantially reduced computational burden and increased compactness and deployability.
Ryan Harne at Ohio State University has developed a wave-energy guiding system that provides an alternative to phase delay technology by utilizing structural topology to enhance directional and spectral sensitivities. These origami-based engineering design techniques provide exceptional versatility and adaptable performance resulting in systems that can be made compact and selectively deployable. Origami-type folded structures provide periodic patterns of planar facets and acoustic arrays are composed from electromechanical transducers positioned on the planar elements, all of which are together driven by one or a few signals. Simple kinematic and mechanical transformations of this folding array topology can therefore govern the directional and spectral sensitivities for wave energy guiidng and steering, in contrast to a multitude of individually controlled signals sent/received from a spatisally-fixed, conventional array of acoustic sources/receivers. The Harne system further includes actuation mechanisms to controllably fold and unfold the structural substrate to provide tunable changes in functionality.
Non-small cell lung cancer (NSCLC), which accounts for 85% of all lung cancers, is one of the leading causes of cancer-related death. Substantial progress has been made in the management of NSCLC as several driver mutations have been identified in cell communication pathways involved in lung tumoriogenesis. One of the major pathways that is often targeted in developmental treatments with the use of inhibitors are proteins called tyrosine kinases. Tyrosine kinase inhibitors (TKI's) are primarily used against tumors with specific activating mutations in the epidermal growth factor receptor (EGFR). However, treatment with first and even second generation TKI's results in variable clinical responses and is not considered curative as tumor eradication is never achieved. Researchers have identified that EGFR mutant lung cancer cells avoid eradication through pre-existing subclones. Furthermore, treatment with a first generation drug, such as erlotinib, which is designed to target the subclones, is not completely effective and a subpopulation of cells survives. The inherent 'adaptive persistence of this cancer type is a mechanism that needs to be elucidated for the treatment of this complex tumor type.
Dr. Rajeswara Rao Arasada and colleagues at The Ohio State University have identified a novel pathway that facilitates the survival of the subset of cells that make up the 'adaptive persisters'. The researchers discovered that with EGFR inhibition, there is a rapid induction of β-catenin signaling, a protein that interacts with a cell signaling protein Notch3. Furthermore, they have demonstrated that with pre-clinical xenograft mouse model that the combination of EGFR-TKI and β-catenin inhibitor, PRI-724, blocks this phenomenon, thus decreasing tumor burden and improving both recurrence free survival and overall survival.
Based on their initial pre-clinical discovery, Dr. Arasada and colleagues have developed a method for clinically diagnosing EGFR TKI drug resistance in a subject with an EGFR mutated NSCLC through a simple blood test. The blood test uses plasminogen activator inhibitor-1 (PAI-1) as a prognostic marker to determine the degree of resistance in the patient before and after treatment. Additionally, the researchers have developed a method for treating EGFR TKI drug resistance through the use of an effective dose of β-catenin inhibitor. With these novel and exciting breakthroughs, Dr. Arasada and colleagues have developed both a novel diagnosis and treatment for EGFR TKI drug resistance in NSCLC that will undoubtedly impact the future treatment of NSCLC.
Oncology therapeutic drug treatments
Novel diagnosis and treatment of drug-resistant cancer
Potential reduction in cancer-related death
Particle methods are a class of computational algorithms used to estimate potential outcomes of a system or process. Although popular for their simplicity and scalability, the use of fixed sized "particle ensembles" renders the simulations unable to provide performance guarantees in quantifying system uncertainty. Thus, there is no way of knowing how accurate the generated forecast is. Since the state-of-the-art algorithm cannot guarantee achieving the desired level of accuracy, users often "over-compute" by using larger ensembles in the hopes that it works over the entire duration of forecasting. For moderately to highly complex systems, this is likely to be too computationally burdensome. Moreover, over-computing still does not provide any guarantees of accuracy.
This invention provides system forecasts with guaranteed performance while also providing the flexibility of using the smallest possible ensemble to achieve the user's desired accuracy. The particle ensemble is dynamic and adaptable, making the algorithm more computationally efficient than the state of the art. As a result, it delivers system forecasts with a guaranteed estimation accuracy of quantities of interest over the entire duration of the forecast, thereby enabling more robust decision making and system prognostics.
The forecasts derived by this platform are intended for use in a wide range of industries, such as:
Mg-Zn-Ca-based alloys are the most promising alloy system for bone implant applications mainly due to their superior biocompatibility. For fixation applications, the ideal is a material strong enough to hold while healing (2-4 months) yet being fully resorbed after the bones have healed (6-24 months). For patient-specific (3D-printed) fixation hardware made of such alloys, various heat treatment processes have been employed to enhance the mechanical or corrosion properties of fixation devices. Most of these efforts have focused on enhancement of a single property such as mechanical strength, biocompatibility or biocorrosion. However, for optimal results, the use of heat treatment for developing improved Mg-Zn-Ca-based bone fixations should address all of the following: (i) proper choice of alloy chemical composition; (ii) proper choice of the heat treatment process and parameters; (iii) asessment of mechanical properties; and (iv) assessment of biocorrosion properties after heat treatment.
This invention is a joint development of the University of Toledo and Ohio State University. It comprises a process in which a novel Mg-Zn-Ca-based alloy is cast and then heat-treated - in particular, solution-treated, quenched and age-hardened. The chemical composition of the cast Mg-Zn-Ca alloy is chosen to obtain the optimum age hardening effect after the heat treatment process. Heat treatment processes and parameters are also chosen carefully to optimize results. For example, the alloy was aged at different age hardening temperatures to determine the temperature that results in the highest mechanical and corrosion resistance. Addition of Mn to the alloy was found to further enhance mechanical and corrosion properties. The ternary or quaternary alloy is coated with a biocompatible ceramic coating using micro arc oxidation (MAO) followed by additional ceramic layering and a sintering process. This coating process determines when resorption begins, allowing for a tailored biocorrosion rate specific to the patient's needs.
A primary complaint of hearing-impaired (HI) listeners is poor speech recognition in background noise. This issue can be quite debilitating and persists despite considerable efforts to improve hearing technology. The primary limitation resulting from sensorineural hearing impairment of cochlear origin involves elevated audiometric thresholds and resulting limited audibility. Despite considerable effort, monaural (single-microphone) algorithms capable of increasing the intelligibility of speech in noise have remained elusive. Successful development of such an algorithm is especially important for hearing-impaired (HI) listeners, given their particular difficulty in noisy backgrounds.
Researchers at The Ohio State University, led by Dr. Eric Healy, have developed a machine-learning algorithm using time-frequency masking to separate speech from noise in audio signals of various signal-to-noise ratios. The algorithm combines the computational simplicity of an Ideal Binary Mask (IBM) with the sound quality of an Ideal Ratio Mask (IRM) in order to attain intelligibility results equal to or superior to the IRM at computational loads only marginally larger than the IBM.
An IBM is a binary system that assigns a value of 0 or 1 to each time-frequency unit based on its signal-to-noise ratio (SNR). Units with a poor SNR are assigned a 0 and attenuated, resulting in an output signal containing only t-f units dominated by speech. In IRM signals are again attenuated based on a local criterion, but they can be assigned any value between 0 and 1 resulting in a smoother output. The Ideal Quantized Mask (IQM) developed by OSU’s research team utilizes both methods. Instead of IBM’s two attenuation levels, IQM classifies each t-f unit into any number of discrete categories by way of a machine-learning algorithm. While this means IQM could theoretically have an infinite number of categories, with just eight attenuation levels IQM achieves IRM level intelligibility far higher than IBM without the need to engage in IRM’s regression calculations.