Tomohiro Konno

The development of polymeric biomaterials for modern pharmaceutical formulations has attracted considerable attention, given the recent trend of advanced pharmaceutical modalities that have increased molecular weight and hydrophobicity. Phospholipid polymers, such as poly[2-methacryloyloxyethyl phosphorylcholine (MPC)- co - n -butyl methacrylate (BMA)] (PMB) is a promising candidate for use as cytocompatible drug carrier materials. PMB is a bioinspired polymer that mimics the plasma membrane and offers superior biomaterial properties, such as bioinert and antibiofouling characteristics. By adjusting the copolymer composition and molecular weight, water-soluble PMB can be synthesized, resulting in the formation of polymeric lipid nanoaggregates. Water-soluble PMB can stably solubilize poorly water-soluble drugs. Moreover, PMB exhibits bidirectional permeation across the plasma membrane. This polymer lipid nanoaggregate, which diffuses and penetrates through intra- and extracellular spaces, was designed as a “cell shuttle”. This focus review discusses studies of cell shuttles in the context of multipharmaceutical modalities, including the effects of polymer architecture on intracellular internalization, the intracellular delivery of hydrophobic compounds, and the cytosolic delivery of proteins. Finally, the utilities of the phospholipid polymer-based cell shuttle are outlined from the perspective of pharmaceutical sciences.

Protein therapeutics have gained market share in the medical community worldwide. However, proteins cannot cross cell membranes, and they can be degraded in the intracellular or extracellular environment. Materials that are used in pharmaceutical formulations play crucial roles in allowing the proteins they deliver to perform the appropriate functions. The copolymer of 2-methacryloyloxyethyl phosphorylcholine (MPC) and n -butyl methacrylate (BMA) is a phospholipid-mimicking polymer that has excellent biocompatibility and direct cell-penetrating properties. In this study, the phospholipid polymer-based bioconjugation polymer poly[MPC- co -BMA- co - N -succinimidyloxycarbonyl tetra(ethylene glycol) methacrylate (MENHS)] (PMBS) was synthesized and conjugated with OVA: ovalbumin (as a model protein). We investigated the effect of the PMBS and OVA mixing ratio on the cellular internalization of OVA to evaluate the ability of this formulation to control immune responses in a murine dendritic cell line. The bioconjugate of PMBS-OVA formed polymolecular associations in aqueous solution. The PMBS-OVA conjugate that contained a greater amount of PMBS delivered OVA into the cytosol at 4 °C, which is a condition that inhibits the endocytic pathway. Although the PMBS-OVA conjugate did not affect cytokine production, it increased antigen presentation via major histocompatibility complex class I. Therefore, we successfully prepared a bioconjugate that exhibits excellent biocompatibility and cans directly deliver a protein into the cytosol using phospholipid polymers.

Highly stereochemically controlled polymers were successfully synthesized from levoglucosenone (LGO), derived from cellulose. Since its discovery in the 1970s, the reactivity of LGO has been widely studied in organic chemistry, owing to its diverse functional groups that serve as linkages for polymer formation. However, most of the previous methods for synthesizing polymers from LGO lacked precise control over regio- and stereochemistry, making stereoselective polymerization from LGO a persistent challenge. Although the ketone moiety in LGO is typically reduced before polymerization, a new LGO polymer was designed, containing a C=N bond obtained by condensation with dicarboxylic dihydrazide. NMR measurements revealed that condensation occurred with high stereoselectivity to produce the E-isomer. This selectivity extended from the model compound to polymer synthesis, achieving high E / Z selectivity. The resulting polymer exhibited optical rotation (up to +89), indicating its potential as a chiral polymer. In spite of these polymers showed high tolerance toward many solvents, they were degradable in water with a simple chemical treatment. The proposed approach facilitates the development of sustainable, high-performance materials that can address both environmental and industrial needs.

Highly stereochemically controlled polymers were successfully synthesized from levoglucosenone (LGO), derived from cellulose. Since its discovery in the 1970s, the reactivity of LGO has been widely studied in organic chemistry, owing to its diverse functional groups that serve as linkages for polymer formation. However, most of the previous methods for synthesizing polymers from LGO lacked precise control over regio- and stereochemistry, making stereoselective polymerization from LGO a persistent challenge. Although the ketone moiety in LGO is typically reduced before polymerization, a new LGO polymer was designed, containing a C=N bond obtained by condensation with dicarboxylic dihydrazide. NMR measurements revealed that condensation occurred with high stereoselectivity to produce the E-isomer. This selectivity extended from the model compound to polymer synthesis, achieving high E / Z selectivity. The resulting polymer exhibited optical rotation (up to +89), indicating its potential as a chiral polymer. In spite of these polymers showed high tolerance toward many solvents, they were degradable in water with a simple chemical treatment. The proposed approach facilitates the development of sustainable, high-performance materials that can address both environmental and industrial needs.

Label-free measurement is essential to understand the metabolism of drug molecules introduced into cells. Raman imaging is a powerful method to investigate intracellular drug molecules because it provides in situ label-free observation of introduced molecules. In this study, we propose that Raman imaging can be used not only to observe the intracellular distribution of drug molecules but also to quantitatively visualize the concentration distribution reflecting each organelle in a single living cell using the Raman band of extracellular water as an intensity standard. We dissolved poorly water-soluble all-trans-retinoic acid (ATRA) in water using a cytocompatible amphiphilic phospholipid polymer, poly[2-methacryloyloxyethyl phosphorylcholine-co-n-butyl methacrylate] (PMB) as a solubilizing reagent, introduced it into cells, and obtained the intracellular concentration distribution of ATRA. ATRA was concentrated in the cells and mainly localized to mitochondria and lipid droplets, interacting strongly with mitochondria and weakly with lipid droplets. Poorly water-soluble β-carotene was also introduced into cells using PMB but was not concentrated intracellularly, indicating that β-carotene does not interact specifically with intracellular molecules. We established a protocol for the solubilization and intracellular uptake of poorly water-soluble molecules using PMB and obtaining their concentration distribution using Raman microscopy.

High-level information processing in the mammalian cortex requires both segregated processing in specialized circuits and integration across multiple circuits. One possible way to implement these seemingly opposing demands is by flexibly switching between states with different levels of synchrony. However, the mechanisms behind the control of complex synchronization patterns in neuronal networks remain elusive. Here, we use precision neuroengineering to manipulate and stimulate networks of cortical neurons in vitro, in combination with an in silico model of spiking neurons and a mesoscopic model of stochastically coupled modules to show that (i) a modular architecture enhances the sensitivity of the network to noise delivered as external asynchronous stimulation and that (ii) the persistent depletion of synaptic resources in stimulated neurons is the underlying mechanism for this effect. Together, our results demonstrate that the inherent dynamical state in structured networks of excitable units is determined by both its modular architecture and the properties of the external inputs.

Reservoir computing is a machine learning paradigm that transforms the transient dynamics of high-dimensional nonlinear systems for processing time-series data. Although the paradigm was initially proposed to model information processing in the mammalian cortex, it remains unclear how the nonrandom network architecture, such as the modular architecture, in the cortex integrates with the biophysics of living neurons to characterize the function of biological neuronal networks (BNNs). Here, we used optogenetics and calcium imaging to record the multicellular responses of cultured BNNs and employed the reservoir computing framework to decode their computational capabilities. Micropatterned substrates were used to embed the modular architecture in the BNNs. We first show that the dynamics of modular BNNs in response to static inputs can be classified with a linear decoder and that the modularity of the BNNs positively correlates with the classification accuracy. We then used a timer task to verify that BNNs possess a short-term memory of several 100 ms and finally show that this property can be exploited for spoken digit classification. Interestingly, BNN-based reservoirs allow categorical learning, wherein a network trained on one dataset can be used to classify separate datasets of the same category. Such classification was not possible when the inputs were directly decoded by a linear decoder, suggesting that BNNs act as a generalization filter to improve reservoir computing performance. Our findings pave the way toward a mechanistic understanding of information representation within BNNs and build future expectations toward the realization of physical reservoir computing systems based on BNNs.

The zwitterionic phospholipid polymer poly(2-methacryloyloxyethyl phosphorylcholine-co-n-butyl methacrylate) (PMB) is amphiphilic copolymer, and it has been reported to directly penetrate cell membranes and have good cytocompatibility. Conventional PMBs are linear-type random copolymers that are polymerized by a free radical polymerization technique. In contrast, star-shaped polymers, or simple branched-type polymers, have unique properties compared to the linear types, for example, a viscosity based on the effect of the excluded volume. In this study, a branched architecture was introduced into a PMB molecular structure, and a 4-armed star-shaped PMB (4armPMB) was synthesized by an atom transfer radical polymerization (ATRP) technique known as living radical polymerization. Linear-type PMB was also synthesized using ATRP. The effects of the polymer architecture on cytotoxicity and cellular uptake were investigated. Both 4armPMB and LinearPMB were successfully synthesized, and these polymers were verified to be water soluble. Pyrene fluorescence in the polymer solution indicated that the architecture had no effect on the behavior of the polymer aggregates. In addition, these polymers caused no cytotoxicity or cell membrane damage. The 4armPMB and LinearPMB penetrated into the cells after a short incubation period, with similar rates. In contrast, the 4armPMB showed a quicker back-diffusion from the cells than that of LinearPMB. The 4armPMB showed fast cellular internalization and exiting behaviors.

Reservoir computing is a machine learning paradigm that transforms the transient dynamics of high-dimensional nonlinear systems for processing time-series data. Although reservoir computing was initially proposed to model information processing in the mammalian cortex, it remains unclear how the non-random network architecture, such as the modular architecture, in the cortex integrates with the biophysics of living neurons to characterize the function of biological neuronal networks (BNNs). Here, we used optogenetics and fluorescent calcium imaging to record the multicellular responses of cultured BNNs and employed the reservoir computing framework to decode their computational capabilities. Micropatterned substrates were used to embed the modular architecture in the BNNs. We first show that modular BNNs can be used to classify static input patterns with a linear decoder and that the modularity of the BNNs positively correlates with the classification accuracy. We then used a timer task to verify that BNNs possess a short-term memory of ~1 s and finally show that this property can be exploited for spoken digit classification. Interestingly, BNN-based reservoirs allow transfer learning, wherein a network trained on one dataset can be used to classify separate datasets of the same category. Such classification was not possible when the input patterns were directly decoded by a linear decoder, suggesting that BNNs act as a generalization filter to improve reservoir computing performance. Our findings pave the way toward a mechanistic understanding of information processing within BNNs and, simultaneously, build future expectations toward the realization of physical reservoir computing systems based on BNNs.