Polylactic acid (PLA) is the most commonly used biodegradable polymer in clinical applications today. Examples range from drug delivery systems, tissue engineering, temporary and long-term implantable devices; constantly expanding to new fields. This is owed greatly to the polymer's favorable biocompatibility and to its safe degradation products. Once coming in contact with biological media, the polymer begins breaking down, usually by hydrolysis, into lactic acid (LA) or to carbon dioxide and water. These products are metabolized intracellularly or excreted in the urine and breath. Bacterial infection and foreign-body inflammation enhance the breakdown of PLA, through the secretion of enzymes that degrade the polymeric matrix. The biodegradation occurs both on the surface of the polymeric device and inside the polymer body, by diffusion of water between the polymer chains. The median half-life of the polymer is 30 weeks; however, this can be lengthened or shortened to address the clinical needs. Degradation kinetics can be tuned by determining the molecular composition and the physical architecture of the device. Using L-or D-chirality of the LA will greatly slow or lengthen the degradation rates, respectively. Despite the fact that this polymer is more than 150 years old, PLA remains a fertile platform for biomedical innovation and fundamental understanding of how artificial polymers can safely coexist with biological systems.

Artificial intelligence (AI) and nanotechnology are two fields that are instrumental in realizing the goal of precision medicine—tailoring the best treatment for each cancer patient. Recent conversion between these two fields is enabling better patient data acquisition and improved design of nanomaterials for precision cancer medicine. Diagnostic nanomaterials are used to assemble a patient‐specific disease profile, which is then leveraged, through a set of therapeutic nanotechnologies, to improve the treatment outcome. However, high intratumor and interpatient heterogeneities make the rational design of diagnostic and therapeutic platforms, and analysis of their output, extremely difficult. Integration of AI approaches can bridge this gap, using pattern analysis and classification algorithms for improved diagnostic and therapeutic accuracy. Nanomedicine design also benefits from the application of AI, by optimizing material properties according to predicted interactions with the target drug, biological fluids, immune system, vasculature, and cell membranes, all affecting therapeutic efficacy. Here, fundamental concepts in AI are described and the contributions and promise of nanotechnology coupled with AI to the future of precision cancer medicine are reviewed.

Overexpressed extracellular matrix (ECM) in pancreatic ductal adenocarcinoma (PDAC) limits drug penetration into the tumor and is associated with poor prognosis. Here, we demonstrate that a pretreatment based on a proteolytic-enzyme nanoparticle system disassembles the dense PDAC collagen stroma and increases drug penetration into the pancreatic tumor. More specifically, the collagozome, a 100 nm liposome encapsulating collagenase, was rationally designed to protect the collagenase from premature deactivation and prolonged its release rate at the target site. Collagen is the main component of the PDAC stroma, reaching 12.8 ± 2.3% vol in diseased mice pancreases, compared to 1.4 ± 0.4% in healthy mice. Upon intravenous injection of the collagozome, ∼1% of the injected dose reached the pancreas over 8 h, reducing the level of fibrotic tissue to 5.6 ± 0.8%. The collagozome pretreatment allowed increased drug penetration into the pancreas and improved PDAC treatment. PDAC tumors, pretreated with the collagozome followed by paclitaxel micelles, were 87% smaller than tumors pretreated with empty liposomes followed by paclitaxel micelles. Interestingly, degrading the ECM did not increase the number of circulating tumor cells or metastasis. This strategy holds promise for degrading the extracellular stroma in other diseases as well, such as liver fibrosis, enhancing tissue permeability before drug administration.

Lipid nanoparticles are used widely as anticancer drug and gene delivery systems. Internalizing into the target cell is a prerequisite for the proper activity of many nanoparticulate drugs. We show here, that the lipid composition of a nanoparticle affects its ability to internalize into triplenegative breast cancer cells. The lipid headgroup had the greatest effect on enhancing cellular uptake compared to other segments of the molecule. Having a receptor-targeted headgroup induced the greatest increase in cellular uptake, followed by cationic amine headgroups, both being superior to neutral (zwitterion) phosphatidylcholine or to negatively-charged headgroups. The lipid tails also affected the magnitude of cellular uptake. Longer acyl chains facilitated greater liposomal cellular uptake compared to shorter tails, 18:0>16:0>14:0. When having the same lipid tail length, unsaturated lipids were superior to saturated ones, 18:1>18:0. Interestingly, liposomes composed of phospholipids having 14:0 or 12:0-carbon-long-tails, such as DMPC and DLPC, decreased cell viability in a concertation dependent manner, due to a destabilizing effect these lipids had on the cancer cell membrane. Contrarily, liposomes composed of phospholipids having longer carbon tails (16:0 and 18:0), such as DPPC and HSPC, enhanced cancer cell proliferation. This effect is attributed to the integration of the exogenous liposomal lipids into the cancer-cell membrane, supporting the proliferation process. Cholesterol is a common lipid additive in nanoscale formulations, rigidifying the membrane and stabilizing its structure. Liposomes composed of DMPC (14:0) showed increased cellular uptake when enriched with cholesterol, both by endocytosis and by fusion. Contrarily, the effect of cholesterol on HSPC (18:0) liposomal uptake was minimal. Furthermore, the concentration of nanoparticles in solution affected their cellular uptake. The higher the concentration of nanoparticles the greater the absolute number of nanoparticles taken up per cell. However, the efficiency of nanoparticle uptake, i.e. the percent of nanoparticles taken up by cells, decreased as the concentration of nanoparticles increased. This

Targeting neurons in breast cancer with anesthetic nanoparticles inhibits nerve-cancer stimulation and tumor progression.