Chemotherapeutic treatment with conventional drug formulations pose numerous challenges, such as poor solubility, high cytotoxicity and serious off‐target side effects, low bioavailability, and ultimately subtherapeutic tumoral concentration leading to poor therapeutic outcomes. In the field of Nanomedicine, advances in nanotechnology have been applied with great success to design and develop novel nanoparticle‐based formulations for the treatment of various types of cancer. The approval of the first nanomedicine, Doxil® (liposomal doxorubicin) in 1995, paved the path for further development for various types of novel delivery platforms. Several different types of nanoparticles, especially organic (soft) nanoparticles (liposomes, polymeric micelles, and albumin‐bound nanoparticles), have been developed and approved for several anticancer drugs. Nanoparticulate drug delivery platform have facilitated to overcome of these challenges and offered key advantages of improved bioavailability, higher intra‐tumoral concentration of the drug, reduced toxicity, and improved efficacy. This review introduces various commonly used nanoparticulate systems in biomedical research and their pharmacokinetic (PK) attributes, then focuses on the various physicochemical and physiological factors affecting the in vivo disposition of chemotherapeutic agents encapsulated in nanoparticles in recent years. Further, it provides a review of the current landscape of soft nanoparticulate formulations for the two most widely investigated anticancer drugs, paclitaxel, and doxorubicin, that are either approved or under investigation. Formulation details, PK profiles, and therapeutic outcomes of these novel strategies have been discussed individually and in comparison, to traditional formulations. This article is categorized under: Nanotechnology Approaches to Biology > Cells at the Nanoscale Diagnostic Tools > In Vivo Nanodiagnostics and Imaging Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease

Design of Experiments (DOE) is useful tool for formulation optimization. In present study, Design Expert® software (version 13) was used for optimization of Poly(dl‐lactide‐co‐glycolide) (50:50) (PLGA) nanoparticles (NPs). Capecitabine, an approved anticancer drug with short half‐life, was used as model drug. Capecitabine NPs can minimize dosing frequency and improve patient compliance. Effect of two numeric (drug and polymer amount) and one categoric factor (pH of W2 phase) was studied on NP Size and Entrapment Efficiency (EE). Drug amount and pH significantly influenced EE. Polymer amount significantly influenced NP size. Methods PLGA NPs were prepared by double emulsion technique. Aqueous drug solution (W1) was emulsified with polymer solution in ethyl acetate using probe sonicator (Misonix) for 2 minutes. Formed primary emulsion was sonicated with second aqueous phase (W2) containing stabilizer Kolliphor®P188 for 1 minute to produce double emulsion. Organic solvent and excess water were evaporated at 600 rpm, 60°C for 1.5 hr and NPs were washed thrice (14,000 rpm, 4°C for 45 min) Lyophilized NPs were sonicated with acetonitrile for 5 min and EE was analyzed using UV/Vis spectrometer at ʎmax304nm. Particle size was determined using Malvern Nano‐ZS. Results Experimental data were fitted into models of increasing polynomial complexity. EE data is best described by two‐factor interaction model with R² of 0.9145. ANOVA results show model is significant. Predicted R²of 0.8076 is in reasonable agreement with adjusted R² of 0.8824. Drug amount, pH of W2 phase and their interaction are significant (p<0.05). Higher drug amount decreases EE which maybe because polymer amount was not sufficient to encapsulate increased drug amount and more drug migrated to W2 phase. Lower EE at pH 4.5 might be described by drug leaching into W2 phase due to better drug solubility. Diagnostic normal plot, perturbation plots, interaction plots and 3D‐surface plot confirm model robustness. Size data was best fitted to linear model showing polymer amount being significant. Higher polymer amount might have decreased dispersion efficiency of first emulsion into W2 phase and led to fusion of semi‐formed particles to larger size. Numerical Optimization The desired goals were set to minimize polymer amount (to increase drug amount per unit weight of NPs), to minimize drug amount and the pH was set at 7.4 to maximize EE. Predicted optimum responses were checked experimentally. Batch prepared with 120mg polymer and 25mg capecitabine has average size of 111.3 nm (predicted value 116.8nm) and EE of 38% (predicted value 40%), both with 5% coefficient of variance. Conclusion DOE gives an opportunity to look at individual and combined impact of various factors. It can provide valid conclusions with minimal experimental runs, time, and cost. Similar DOE approaches can be used for other PLGA‐drug formulations optimization. Conflict of Interest Authors declare no conflicts of interest.