Age‐related central neurodegenerative diseases, such as Alzheimer's and Parkinson's disease, are a rising public health concern and have been plagued by repeated drug development failures. The complex nature and poor mechanistic understanding of the etiology of neurodegenerative diseases has hindered the discovery and development of effective disease‐modifying therapeutics. Quantitative systems pharmacology models of neurodegeneration diseases may be useful tools to enhance the understanding of pharmacological intervention strategies and to reduce drug attrition rates. Due to the similarities in pathophysiological mechanisms across neurodegenerative diseases, especially at the cellular and molecular levels, we envision the possibility of structural components that are conserved across models of neurodegenerative diseases. Conserved structural submodels can be viewed as building blocks that are pieced together alongside unique disease components to construct quantitative systems pharmacology (QSP) models of neurodegenerative diseases. Model parameterization would likely be different between the different types of neurodegenerative diseases as well as individual patients. Formulating our mechanistic understanding of neurodegenerative pathophysiology as a mathematical model could aid in the identification and prioritization of drug targets and combinatorial treatment strategies, evaluate the role of patient characteristics on disease progression and therapeutic response, and serve as a central repository of knowledge. Here, we provide a background on neurodegenerative diseases, highlight hallmarks of neurodegeneration, and summarize previous QSP models of neurodegenerative diseases.

Pain-related sensory input is processed in the spinal cord before being relayed to the brain. That processing profoundly influences whether stimuli are correctly or incorrectly perceived as painful.Significant advances have been made in identifying the types of excitatory and inhibitory neurons that comprise the spinal dorsal horn (SDH), and there is some information about how neuron types are connected, but it remains unclear how the overall circuit processes sensory input. To explore SDH circuit function, we developed a computational model of the circuit that is tightly constrained by experimental data. Our model comprises conductance-based neuron models that reproduce the characteristic firing patterns of excitatory and inhibitory neurons. Excitatory neuron subtypes defined by calretinin, somatostatin, delta opioid receptor, protein kinase C gamma, or vesicular glutamate transporter 3 expression or by transient/central spiking/morphology, and inhibitory neuron subtypes defined by parvalbumin or dynorphin expression or by islet morphology were synaptically connected according to available qualitative data. Synaptic weights were adjusted to produce firing in projection neurons, defined by neurokinin-1 expression, matching experimentally measured responses to a range of mechanical stimulus intensities. Input to the circuit was provided by three types of afferents whose firing rates were also matched to experimental data. To validate our model, we ablated specific neuron types or applied other changes and compared model output with experimental data after equivalent manipulations. The resulting model provides a valuable tool for testing hypotheses in silico to plan novel experiments on SDH circuit dynamics and function.

Ground-state and finite-temperature properties of S = 1/2 Heisenberg ladders with a ferromagnetic leg, an antiferromagnetic leg, and antiferromagnetic rungs are studied. It is shown that a partial ferrimagnetic phase extends over a wide parameter range in the ground state. The numerical results are supported by an analytical calculation based on a mapping onto the nonlinear σ model and a perturbation calculation from the strong-rung limit. It is shown that the partial ferrimagnetic state is a spontaneously magnetized Tomonaga-Luttinger liquid with incommensurate magnetic correlation, which is confirmed by a DMRG calculation. The finite-temperature magnetic susceptibility is calculated using the thermal pure quantum state method. It is suggested that the susceptibility diverges as T −2 in the ferrimagnetic phases as in the case of ferromagnetic Heisenberg chains. IntroductionFerrimagnetism in one-dimensional quantum magnets has been attracting broad interest in condensed matter physics. Conventional ferrimagnetism in unfrustrated spin chains can be understood on the basis of the LiebMattis (LM) theorem, 1) for which the spontaneous magnetization is quantized to the values expected from the LM theorem. 2, 3) This type of ferrimagnetism is called LM ferrimagnetism. For weak frustration, LM ferrimagnetism often remains stable. Another type of quantum ferrimagnetism induced by frustration for which the spontaneous magnetization varies continuously with the strength of frustration is called partial ferrimagnetism. [4][5][6][7][8][9][10][11][12][13] In this case, the spontaneous magnetization is not quantized to a specific value. In many numerical examples, 7-12) partial ferrimagnetism is accompanied by an incommensurate quasi-long-range modulation of the magnetization. Recently, an analytical approach using the nonlinear σ model has been proposed to understand the partial ferrimagnetism of this kind. 14) It is proposed that this phase can be characterized as a spontaneously magnetized Tomonaga-Luttinger liquid (SMTLL).In the present work, we investigate the partial ferrimagnetism in S = 1/2 Heisenberg ladders with a ferromagnetic leg, an antiferromagnetic leg, and antiferromagnetic rungs. In the absence of rung interactions, the system decouples to a spin-1/2 antiferromagnetic chain and a spin-1/2 ferromagnetic chain. Hence, the ground state has magnetization M = L/2, where L is the length along the legs. On the other hand, in the strong-rung

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1. The metabolism and pharmacokinetics of S-777469 were investigated after a single oral administration of [14C]-S-777469 to healthy human subjects. 2. Total radioactivity was rapidly and well absorbed in humans, with Cmax of 11,308 ng eq. of S-777469/ml at 4.0 h. The AUCinf ratio of unchanged S-777469 to total radioactivity was approximately 30%, indicating that S-777469 was extensively metabolized in humans. 3. The metabolite profiling in human plasma showed that S-777469 5-carboxymethyl (5-CA) and S-777469 5-hydroxymethyl (5-HM) were the main circulating metabolites, and the AUCinf ratio of 5-CA and 5-HM to total radioactivity were 24 and 9.1%, respectively. These data suggest that S-777469 was subsequently metabolized to 5-CA in humans although the production amount of 5-CA was extremely low in human hepatocytes. 4. Total radioactivity was mainly excreted via the feces, with 5-CA and 5-HM being the main excretory metabolites in feces and urine. Urinary excretion of 5-CA was comparable with that of 5-HM, whereas fecal excretion of 5-CA was lower than that of 5-HM. 5. In conclusion, the current mass balance study revealed the metabolic and pharmacokinetic properties of S-777469 in humans. These data should be useful to judge whether or not the safety testing of metabolite of S-777469 is necessary.

1. The drug metabolism and pharmacokinetics of S-777469 were investigated in in vitro (rat, dog and human) and in in vivo (rats and dogs). 2. S-777469 was rapidly and well absorbed, with bioavailability values ranging from 50 to 70% in rats and dogs, almost all drug radioactivity was excreted into the feces via bile within 48 h. Thus, good pharmacokinetics of S-777469 (e.g. systemic exposure and excretion rate) would be anticipated in humans. 3. In vitro metabolism of S-777469 was qualitatively similar in rat, dog and human hepatocytes. S-777469 acyl glucuronide, S-777469 5-hydroxymethyl and S-777469 4-hydroxycyclohexane were the main metabolites in rats, dogs and humans. In vivo metabolism in rats and dogs showed good qualitative agreement with in vitro metabolism, and no metabolites exceeded 10% of total radioactivity in rat and dog plasma. 4. No unique metabolites were observed in human hepatocytes. Therefore, rats and dogs were thought to be appropriate species for non-clinical toxicity studies. 5. In conclusion, these data should be useful for the characterization of the pharmacokinetic properties of S-777469 and the estimation of its pharmacokinetic fate in humans.

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Pain-related sensory input is processed in the spinal cord before being relayed to the brain. That processing profoundly influences whether stimuli are correctly or incorrectly perceived as painful. Significant advances have been made in identifying the types of excitatory and inhibitory neurons that comprise the spinal dorsal horn (SDH), and there is some information about how neuron types are connected, but it remains unclear how the overall circuit processes sensory input. To explore SDH circuit function, we developed a computational model of the circuit that is tightly constrained by experimental data. Our model comprises conductance-based neuron models that reproduce the characteristic firing patterns of excitatory and inhibitory neurons. Excitatory neuron subtypes defined by calretinin, somatostatin, delta opioid receptor, protein kinase C gamma, or vesicular glutamate transporter 3 expression or by transient/central spiking/morphology, and inhibitory neuron subtypes defined by parvalbumin or dynorphin expression or by islet morphology were synaptically connected according to available qualitative data. Synaptic weights were adjusted to produce firing in projection neurons, defined by neurokinin-1 expression, matching experimentally measured responses to a range of mechanical stimulus intensities. Input to the circuit was provided by three types of afferents whose firing rates were also matched to experimental data. To validate our model, we ablated specific neuron types or applied other changes and compared model output with experimental data after equivalent manipulations. The resulting model provides a valuable tool for testing hypotheses in silico to plan novel experiments on SDH circuit dynamics and function.