Assessing trends in cytokine-CYP drug interaction and relevance to drug dosing.

The regulation of drug-metabolizing enzymes and transporters by cytokines has been extensively studied, in vitro and in clinic. Cytokine-mediated suppression of CYPs or drug transporters may increase or decrease the systemic clearance of drug substrates that are primarily cleared via these pathways; neutralization of cytokines by therapeutic proteins may thereby alter systemic exposures of such drug substrates. The FDA recommends evaluating such clinical drug interactions during clinical development and has provided labeling recommendations for therapeutic proteins. To determine the clinical relevance of these drug interactions to dose adjustments, trends in steady-state exposures (AUCss) of CYP-sensitive substrates co-administered with cytokine modulators as reported in the UW DIDB were extracted and examined for each of the CYPs. Co-administration of CYP3A (midazolam/simvastatin), CYP2C19 (omeprazole), or CYP1A2 (caffeine/tizanidine) substrates with anti-IL-6 and with anti-IL-23 therapeutics led to changes in systemic exposures of CYP substrates ranging from ~ -58% to ~35%; no significant trends were observed for CYP2D6 (dextromethorphan) and CYP2C9 (warfarin) substrates. Although none of these changes in systemic exposures have been reported as clinically meaningful, dose adjustment of midazolam for optimal sedation in acute care settings has been reported. Simulated concentration-time profiles of midazolam under conditions of elevated cytokine levels when co-administered with tocilizumab, suggest a ~6-7 fold increase in midazolam clearance suggesting potential implications of cytokine- CYP drug interactions on dose adjustments of sensitive CYP3A substrates in acute care settings. Additionally, this article also provides a brief overview of non-clinical and clinical assessments of cytokine-CYP drug interactions, in drug discovery and development. Significance Statement Significance statement: There has been significant progress in understanding cytokine-mediated drug interactions for CYP-sensitive substrates. This article provides an overview of the progress in this field, including a trend analysis of systemic exposures of CYP-sensitive substrates co-administered with anti-IL-x therapeutics. In addition, the review also provides a perspective of current methods used to assess these drug interactions during drug development, and a focus on individualized medicine, particularly in acute care settings.

Stochastic investigation of HIV infection and the emergence of drug resistance.

Drug-resistant HIV-1 has caused a growing concern in clinic and public health. Although combination antiretroviral therapy can contribute massively to the suppression of viral loads in patients with HIV-1, it cannot lead to viral eradication. Continuing viral replication during sub-optimal therapy (due to poor adherence or other reasons) may lead to the accumulation of drug resistance mutations, resulting in an increased risk of disease progression. Many studies also suggest that events occurring during the early stage of HIV-1 infection (i.e., the first few hours to days following HIV exposure) may determine whether the infection can be successfully established. However, the numbers of infected cells and viruses during the early stage are extremely low and stochasticity may play a critical role in dictating the fate of infection. In this paper, we use stochastic models to investigate viral infection and the emergence of drug resistance of HIV-1. The stochastic model is formulated by a continuous-time Markov chain (CTMC), which is derived based on an ordinary differential equation model proposed by Kitayimbwa et al. that includes both forward and backward mutations. An analytic estimate of the probability of the clearance of HIV infection of the CTMC model near the infection-free equilibrium is obtained by a multitype branching process approximation. The analytical predictions are validated by numerical simulations. Unlike the deterministic dynamics where the basic reproduction number R0 serves as a sharp threshold parameter (i.e., the disease dies out if R0<1 and persists if R0>1), the stochastic models indicate that there is always a positive probability for HIV infection to be eradicated in patients. In the presence of antiretroviral therapy, our results show that the chance of clearance of the infection tends to increase although drug resistance is likely to emerge.

Corrigendum to "A click-ready pH-triggered phosphoramidate-based linker for controlled release of monomethyl auristatin E" [Tetrahedron Lett. Volume 61, Issue 41, 8 October 2020, 152398].

[This corrects the article PMC7665082.].

Deterministic and stochastic models for the epidemic dynamics of COVID-19 in Wuhan, China.

In this paper, deterministic and stochastic models are proposed to study the transmission dynamics of the Coronavirus Disease 2019 (COVID-19) in Wuhan, China. The deterministic model is formulated by a system of ordinary differential equations (ODEs) that is built upon the classical SEIR framework. The stochastic model is formulated by a continuous-time Markov chain (CTMC) that is derived based on the ODE model with constant parameters. The nonlinear CTMC model is approximated by a multitype branching process to obtain an analytical estimate for the probability of a disease outbreak. The local and global dynamics of the disease are analyzed by using the deterministic model with constant parameters, and the result indicates that the basic reproduction number $ \mathcal{R}_0 $ serves as a sharp disease threshold: the disease dies out if $ \mathcal{R}_0\le 1 $ and persists if $ \mathcal{R}_0 > 1 $. In contrast to the deterministic dynamics, the stochastic dynamics indicate that the disease may not persist when $ \mathcal{R}_0 > 1 $. Parameter estimation and validation are performed to fit our ODE model to the public reported data. Our result indicates that both the exposed and infected classes play an important role in shaping the epidemic dynamics of COVID-19 in Wuhan, China. In addition, numerical simulations indicate that a second wave of the ongoing pandemic is likely to occur if the prevention and control strategies are not implemented properly.

A click-ready pH-triggered phosphoramidate-based linker for controlled release of monomethyl auristatin E.

In this work, we developed a novel "click"-ready pH-cleavable phosphoramidate linker for controlled-release of monomethyl auristantin E (MMAE) in antibody- and small molecule-drug conjugates application. This water-soluble linker was found to have tremendous stability at physiological pHs while rapidly releasing its payload at acidic pH. The linker can also be tailored to release payloads of diverse functional groups, broadening its applications.

Optimal control in HIV chemotherapy with termination viral load and latent reservoir.

Although a number of cost-e ective strategies have been proposed for the chemotherapy of HIV infection, the termination level of viral load and latent reservoir is barely considered. However, the viral load at the termination time is an important biomarker because suppressing viral load to below the detection limit is a major objective of current antiretroviral therapy. The pool size of latently infected cells at the termination time may also play a critical role in predicting a rapid viral rebound to the pretreatment level or post-treatment control. In this work, we formulate an optimal control problem by incorporating the termination level in terms of viral load, latently and productively infected T cells into an existing HIV model. The necessary condition for this optimal system is derived using the Pontryagin's maximum principle. Numerical analysis is carried out using Runge-Kutta 4 method for the forward-backward sweep. Our results suggest that introducing the termination viral load into the control provides a better strategy in HIV chemotherapy.