6 research outputs found
Exploring the Dynamics of Knowledge Transfer between University Technology Transfer Offices and Venture Capitalists in New Zealand
No full textTechnology Transfer Offices (TTOs) have long been recognised as integral components of university innovation ecosystems, acting as intermediaries that bridge the gap between academia and industry. A generative relationship between TTOs and venture capitalists (VCs) is often viewed as pivotal for technology transfer, as VCs provide not only financial resources but also non-financial value-adds, such as strategic insight and mentorship to early-stage ventures or university spin-offs (USOs). However, little is known whether interactions with VCs also generate non-financial value-add for TTOs themselves and through which mechanisms such value-add is accomplished. Adopting a relational approach, this thesis examines the dynamics of TTO–VC interactions in New Zealand, aiming to uncover the mechanisms that facilitate effective knowledge transfer between these two entities.
To achieve this, a qualitative research approach was utilised to gather data from ten semi-structured interviews split evenly amongst NZ TTOs and VCs. The study used an abductive approach to theory and found the findings well aligned with existing literature. I find that the effectiveness of technology transfer between TTOs and VCs is driven by key organisational factors: VCs’ dissemination capacity and motivation to teach, TTOs’ absorptive capacity and motivation to learn, and the dynamics of the inter-organisational relationship, such as power relations, trust, and social ties. Findings from this study provide actionable insights for TTOs to better leverage and integrate VCs’ value-added activities into their commercialisation efforts. For VCs, the findings offer insights into TTO-specific challenges in bridging research and commercialisation. By identifying mechanisms and capabilities that enable more effective value-adding knowledge transfer, this thesis guides TTOs and VCs in strengthening their collaboration, fostering strategic partnerships across the technology transfer ecosystem, and driving the success of USOs
Optimizing Ternary Nanocarriers for Stable and Non-Toxic Delivery of Rictor/mTORC2 RNAi Against Triple Negative Breast Cancer
No full textTriple negative breast cancer (TNBC) is a highly aggressive BC subtype with limited molecularly targeted therapies. Aberrant activation of the phosphatidylinositol 3-kinase/ mammalian target of rapamycin (PI3K/mTOR) pathway is often seen in TNBC but attempts to target this pathway have been altogether ineffective for TNBC patients. Less is known regarding the signaling complex mTOR complex 2 (mTORC2) which is a central integrative node of the PI3K/mTOR pathway and regulates pro-oncogenic activities such as tumor cell survival, motility/metastasis, and chemoresistance. However, small molecule inhibitors that potently and selectively block mTORC2 do not exist. Here, we leverage short interfering RNA (siRNA) technology to block expression of Rictor, an mTORC2-required cofactor, to test the therapeutic utility of mTORC2 signaling inhibition in TNBC.
siRNA therapies are a promising strategy for treating diseases that lack druggable targets, but their systemic delivery is limited by rapid kidney clearance, low cellular uptake, and poor endosome disruption. siRNA-carrying nanoparticles (si-NPs) can improve siRNA delivery to target organs but continue to face delivery challenges such as limited stability, off-target toxicities, and suboptimal tumor accumulation. Ternary si-NPs containing siRNA, an NP core-forming polymer, and an NP surface-forming polymer have the potential to improve tumor silencing activity because of the participation of both polymers in siRNA encapsulation and pH-responsive endosome disruptive activity. Through concomitant structure-function optimization of the core-forming polymer ratio and molecular weight, we identified a lead ternary si-NP with enhanced stability, potent tumor gene silencing activity, and minimal toxicity.
To enable therapeutic Rictor knockdown in TNBCs in vivo, we utilized our optimized si-NP for intravenous delivery of siRictor, resulting in robust tumor siRNA accumulation, Rictor knockdown, and mTORC2 inhibition. Selective mTORC2 inhibition using siRictor in vivo decreased tumor cell proliferation, survival, and tumor growth in TNBC tumor-bearing mice, and increased paclitaxel-induced tumor growth inhibition. Together, this work supports Rictor ablation as an effective approach for therapeutic mTORC2-selective blockade and identifies a novel RNAi nanotechnology for treatment of PI3K-active TNBC
Increasing Drug Delivery Efficacy of Drug-Coated Balloons
No full textPeripheral artery disease (PAD), the narrowing of peripheral arteries located in areas such as the arms and legs, is a common disease that affects 12% to 20% of people over 65. Numerous therapies have been developed to treat PAD, the most recent technology being drug-coated balloons. Drug-coated balloons release drugs, such as Paclitaxel (PTX), into the arterial wall during balloon angioplasty to locally treat PAD. Current levels of drug transfer from the balloon to the vascular endothelium are at a low 10%-18%. Our Biomedical Engineering Senior Design project aims to increase drug delivery of drug-coated balloons by manipulating the clinically-controllable variables of drug+excipient formulation, balloon inflation pressure, and balloon inflation time. To study the effects of these variable on drug transfer, we first developed in vitro models of the drug-coated balloon (PTX+Urea and PTX+Shellac formulations) and vascular endothelium (porcine blood vessels). These models were then subjected to uniaxial compression testing to simulate balloon inflation against the vessel wall at set inflation pressures and times. Our study found drug transfer to increase with increasing inflation pressure in the PTX+Urea formulation. In contrast, drug transfer for the PTX+Shellac formulation increased with inflation time. Though we were unable to increase drug transfer past 17%, our study indicates that manipulating the above clinical variables can be a powerful tool towards increasing efficacy of drug-coated balloons
Modeling Topics in DFA-Based Lemmatized Gujarati Text
No full textTopic modeling is a machine learning algorithm based on statistics that follows unsupervised machine learning techniques for mapping a high-dimensional corpus to a low-dimensional topical subspace, but it could be better. A topic model’s topic is expected to be interpretable as a concept, i.e., correspond to human understanding of a topic occurring in texts. While discovering corpus themes, inference constantly uses vocabulary that impacts topic quality due to its size. Inflectional forms are in the corpus. Since words frequently appear in the same sentence and are likely to have a latent topic, practically all topic models rely on co-occurrence signals between various terms in the corpus. The topics get weaker because of the abundance of distinct tokens in languages with extensive inflectional morphology. Lemmatization is often used to preempt this problem. Gujarati is one of the morphologically rich languages, as a word may have several inflectional forms. This paper proposes a deterministic finite automaton (DFA) based lemmatization technique for the Gujarati language to transform lemmas into their root words. The set of topics is then inferred from this lemmatized corpus of Gujarati text. We employ statistical divergence measurements to identify semantically less coherent (overly general) topics. The result shows that the lemmatized Gujarati corpus learns more interpretable and meaningful subjects than unlemmatized text. Finally, results show that lemmatization curtails the size of vocabulary decreases by 16% and the semantic coherence for all three measurements—Log Conditional Probability, Pointwise Mutual Information, and Normalized Pointwise Mutual Information—from −9.39 to −7.49, −6.79 to −5.18, and −0.23 to −0.17, respectively
Structural optimization of siRNA conjugates for albumin binding achieves effective MCL1-directed cancer therapy
No full textAbstract The high potential of siRNAs to silence oncogenic drivers remains largely untapped due to the challenges of tumor cell delivery. Here, divalent lipid-conjugated siRNAs are optimized for in situ binding to albumin to improve pharmacokinetics and tumor delivery. Systematic variation of the siRNA conjugate structure reveals that the location of the linker branching site dictates tendency toward albumin association versus self-assembly, while the lipid hydrophobicity and reversibility of albumin binding also contribute to siRNA intracellular delivery. The lead structure increases tumor siRNA accumulation 12-fold in orthotopic triple negative breast cancer (TNBC) tumors over the parent siRNA. This structure achieves approximately 80% silencing of the anti-apoptotic oncogene MCL1 and yields better survival outcomes in three TNBC models than an MCL-1 small molecule inhibitor. These studies provide new structure-function insights on siRNA-lipid conjugate structures that are intravenously injected, associate in situ with serum albumin, and improve pharmacokinetics and tumor treatment efficacy
Nonviral <i>In Vivo</i> Delivery of CRISPR-Cas9 Using Protein-Agnostic, High-Loading Porous Silicon and Polymer Nanoparticles
No full textThe complexity of CRISPR machinery is a challenge to
its application
for nonviral in vivo therapeutic gene editing. Here,
we demonstrate that proteins, regardless of size or charge, efficiently
load into porous silicon nanoparticles (PSiNPs). Optimizing the loading
strategy yields formulations that are ultrahigh loading>40%
cargo by volumeand highly active. Further tuning of a polymeric
coating on the loaded PSiNPs yields nanocomposites that achieve colloidal
stability under cryopreservation, endosome escape, and gene editing
efficiencies twice that of the commercial standard Lipofectamine CRISPRMAX.
In a mouse model of arthritis, PSiNPs edit cells in both the cartilage
and synovium of knee joints, and achieve 60% reduction in expression
of the therapeutically relevant MMP13 gene. Administered intramuscularly,
they are active over a broad dose range, with the highest tested dose
yielding nearly 100% muscle fiber editing at the injection site. The
nanocomposite PSiNPs are also amenable to systemic delivery. Administered
intravenously in a model that mimics muscular dystrophy, they edit
sites of inflamed muscle. Collectively, the results demonstrate that
the PSiNP nanocomposites are a versatile system that can achieve high
loading of diverse cargoes and can be applied for gene editing in
both local and systemic delivery applications
