An adaptive drug delivery design using neural networks for effective treatment of infectious diseases: A simulation study

An adaptive drug delivery design is presented in this paper using neural networks for effective treatment of infectious diseases. The generic mathematical model used describes the coupled evolution of concentration of pathogens, plasma cells, antibodies and a numerical value that indicates the relative characteristic of a damaged organ due to the disease under the influence of external drugs. From a system theoretic point of view, the external drugs can be interpreted as control inputs, which can be designed based on control theoretic concepts. In this study, assuming a set of nominal parameters in the mathematical model, first a nonlinear controller (drug administration) is designed based on the principle of dynamic inversion. This nominal drug administration plan was found to be effective in curing "nominal model patients" (patients whose immunological dynamics conform to the mathematical model used for the control design exactly. However, it was found to be ineffective in curing "realistic model patients" (patients whose immunological dynamics may have off-nominal parameter values and possibly unwanted inputs) in general. Hence, to make the drug delivery dosage design more effective for realistic model patients, a model-following adaptive control design is carried out next by taking the help of neural networks, that are trained online. Simulation studies indicate that the adaptive controller proposed in this paper holds promise in killing the invading pathogens and healing the damaged organ even in the presence of parameter uncertainties and continued pathogen attack. Note that the computational requirements for computing the control are very minimal and all associated computations (including the training of neural networks) can be carried out online. However it assumes that the required diagnosis process can be carried out at a sufficient faster rate so that all the states are available for control computation.

Effective drug dosage design for treatment of infectious diseases using dynamic inversion

A generic nonlinear mathematical model describing the human immunological dynamics is used to design an effective automatic drug administration scheme. Even though the model describes the effects of various drugs on the dynamic system, this work is confined to the drugs that kill the invading pathogen and heal the affected organ. From a system theoretic point of view, the drug inputs can be interpreted as control inputs, which can be designed based on control theoretic concepts. The controller is designed based on the principle of dynamic inversion and is found to be effective in curing the �nominal model patient� by killing the invading microbes and healing the damaged organ. A major advantage of this technique is that it leads to a closed-form state feedback form of control. It is also proved from a rigorous mathematical analysis that the internal dynamics of the system remains stable when the proposed controller is applied. A robustness study is also carried out for testing the effectiveness of the drug administration scheme for parameter uncertainties. It is observed from simulation studies that the technique has adequate robustness for many �realistic model patients� having off-nominal parameter values as well.

Dual Drug Conjugate Loaded Nanoparticles for the Treatment of Cancer

Two antineoplastic agents, Imatinib (IM) and 5-Fluorouracil (FU) were conjugated by hydrolysable linkers through an amide bond and entrapped in polymeric Human Serum Albumin (HSA) nanoparticles. The presence of dual drugs in a common carrier has the advantage of reaching the site of action simultaneously and acting at different phases of the cell cycle to arrest the growth of cancer cells before they develop chemoresistance. The study has demonstrated an enhanced anticancer activity of the conjugate, and conjugate loaded stealth HSA nanoparticles (NPs) in comparison to the free drug in A-549 human lung carcinoma cell line and Zebra fish embryos (Danio rerio). Hydrolysability of the conjugate has also been demonstrated with complete hydrolysis being observed after 12 h. In vivo pharmacodynamics study in terms of tumor volume and pharmacokinetics in mice for conjugate (IM-SC-FU) and conjugate loaded nanoparticles showed significant anti-cancer activity. The other parameters evaluated were particle size (86nm), Poly Dispersive Index (PDI) (0.209), zeta potential (-49mV), drug entrapment efficiency (96.73%) and drug loading efficiency (89%). Being in stealth mode gives the potential for the NPs to evade Reticulo-Endothelial system (RES), achieve passive targeting by Enhanced Permeation Retention (EPR) effect with controlled release of the therapeutic agent. As the conjugate cleaves into individual drugs in the tumor environment, this promises better suppression of cancer chemoresistance by delivering dual drugs with different modes of action at the same site, thereby synergistically inhibiting the growth of cancerous tissue.