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Novel drug delivery systems (NDDS) are one of the most strategies which enable to overcome the problems related to drug bioavailability. It is the rate and extent to which a drug becomes available to the target tissue after its administration. Most of the new drugs used today have poor bioavailability and are required to be administered at higher doses because only a small fraction of the administered dose is absorbed in the systemic circulation and able to reach the target site. This results in the wastage of major amount of drug and lead to adverse effects. Pharmaceutical technology mainly focuses on enhancing the solubility and permeability of drugs with lower bioavailability. Nanotechnology is the concept used in NDDS that enables a weight reduction of drug particles accompanied by an increase in stability and improved functionality. Various approaches such as nanosuspensions, liposomes, niosomes, nanoemulsions, cubosomes, solid lipid nanoparticles (SLN), nanostructured lipid carriers (NLC), cyclodextrins, phytosome etc., are used for the enhancement of bioavailability. The present review focuses on the different approaches used for bioavailability enhancement along with their advantages and disadvantages.

Nature-inspired optimization is one of the most prevalent research domains with a confounding history that fascinates the research communities. Particle Swarm Optimization (PSO) is one of the well-known optimizer that belongs to the family of nature inspired algorithms. It often suffers from premature convergence leading to a local optimum. To address this, several methods were presented using different network topologies of the particles, but either lacked accuracy or were slow. To solve these problems, a improved version of the Directed Weighted Complex Network Particle Swarm Optimization using Genetic Algorithm (GDWCN-PSO) is presented. This method uses the concept of Genetic Algorithm after each iteration to enhance convergence and diversity. Since most of the real world applications and complex optimization problems involve more than one objective functions so to suit this problem a multi-objective version of GDWCN-PSO is also proposed and validated on standard benchmarks. To demonstrate its applicability in real world applications GDWCN-PSO is applied to solve the optimal key based medical image encryption which is one of the most challenging problems in health IoTs for protecting sensitive and confidential patient data and addressing the major concern of integrity and security of data in today's advanced digital world.