Rationally Designed 2‑in‑1 Nanoparticles Can Overcome Adaptive Resistance in Cancer

The development of resistance is the major cause of mortality in cancer. Combination chemotherapy is used clinically to reduce the probability of evolution of resistance. A similar trend toward the use of combinations of drugs is also emerging in the application of cancer nanomedicine. However, should a combination of two drugs be delivered from a single nanoparticle or should they be delivered in two different nanoparticles for maximal efficacy? We explored these questions in the context of adaptive resistance, which emerges as a phenotypic response of cancer cells to chemotherapy. We studied the phenotypic dynamics of breast cancer cells under cytotoxic chemotherapeutic stress and analyzed the data using a phenomenological mathematical model. We demonstrate that cancer cells can develop adaptive resistance by entering into a predetermined transitional trajectory that leads to phenocopies of inherently chemoresistant cancer cells. Disrupting this deterministic program requires a unique combination of inhibitors and cytotoxic agents. Using two such combinations, we demonstrate that a 2-in-1 nanomedicine can induce greater antitumor efficacy by ensuring that the origins of adaptive resistance are terminated by deterministic spatially constrained delivery of both drugs to the target cells. In contrast, a combination of free-form drugs or two nanoparticles, each carrying a single payload, is less effective, arising from a stochastic distribution to cells. These findings suggest that 2-in-1 nanomedicines could emerge as an important strategy for targeting adaptive resistance, resulting in increased antitumor efficacy

Longitudinal biomarker analysis of infants treated with multiple courses of gentamicin without a change in serum creatinine concentration.

<p>Representative figures demonstrating the longitudinal quantification of the biomarkers KIM-1 (blue; ng/mg. uCr), NGAL (green; ng/mg. uCr), NAG (yellow; IU/mg. uCr) and serum creatinine (red; µmol/L) for three infants treated with gentamicin (A–C). Gentamicin treatment episode and length of treatment (days) are indicated by the black horizontal bar on each figure for that individual patient.</p

Baseline characteristics and clinical signs of neonates treated with gentamicin.

<p>Patients are subdivided according to gestational age. Mean biomarker values presented include samples collected both on and off gentamicin treatment over the whole time course of inclusion in the study.</p

Longitudinal biomarker analysis of infants treated with multiple courses of gentamicin with a change in serum creatinine concentration (AKI).

<p>Representative figures demonstrating the longitudinal quantification of the biomarkers KIM-1 (blue; ng/mg. uCr), NGAL (green; ng/mg. uCr), NAG (yellow; IU/mg. uCr) and serum creatinine (red; µmol/L) for three infants treated with gentamicin (A–C). Gentamicin treatment episode and length of treatment (days) are indicated by the black horizontal bar on each figure for that individual patient.</p

Association between gentamicin treatment and the change in biomarker values.

<p>The mean baseline biomarker values in the absence of any gentamicin treatment were 1.91 ng/mg uCr (95% CI 1.07, 2.76) for KIM-1, 0.13 IU/mg uCr (0.07, 0.19) for NAG, 425.4 ng/mg uCr (162.6, 688.3) for NGAL, and 62.39 µmol/l (53.1, 71.69) for creatinine.</p

Algorithm for Designing Nanoscale Supramolecular Therapeutics with Increased Anticancer Efficacy

In the chemical world, evolution is mirrored in the origin of nanoscale supramolecular structures from molecular subunits. The complexity of function acquired in a supramolecular system over a molecular subunit can be harnessed in the treatment of cancer. However, the design of supramolecular nanostructures is hindered by a limited atomistic level understanding of interactions between building blocks. Here, we report the development of a computational algorithm, which we term Volvox after the first multicellular organism, that sequentially integrates quantum mechanical energy-state- and force-field-based models with large-scale all-atomistic explicit water molecular dynamics simulations to design stable nanoscale lipidic supramolecular structures. In one example, we demonstrate that Volvox enables the design of a nanoscale taxane supramolecular therapeutic. In another example, we demonstrate that Volvox can be extended to optimizing the ratio of excipients to form a stable nanoscale supramolecular therapeutic. The nanoscale taxane supramolecular therapeutic exerts greater antitumor efficacy than a clinically used taxane <i>in vivo</i>. Volvox can emerge as a powerful tool in the design of nanoscale supramolecular therapeutics for effective treatment of cancer

Algorithm for Designing Nanoscale Supramolecular Therapeutics with Increased Anticancer Efficacy

In the chemical world, evolution is mirrored in the origin of nanoscale supramolecular structures from molecular subunits. The complexity of function acquired in a supramolecular system over a molecular subunit can be harnessed in the treatment of cancer. However, the design of supramolecular nanostructures is hindered by a limited atomistic level understanding of interactions between building blocks. Here, we report the development of a computational algorithm, which we term Volvox after the first multicellular organism, that sequentially integrates quantum mechanical energy-state- and force-field-based models with large-scale all-atomistic explicit water molecular dynamics simulations to design stable nanoscale lipidic supramolecular structures. In one example, we demonstrate that Volvox enables the design of a nanoscale taxane supramolecular therapeutic. In another example, we demonstrate that Volvox can be extended to optimizing the ratio of excipients to form a stable nanoscale supramolecular therapeutic. The nanoscale taxane supramolecular therapeutic exerts greater antitumor efficacy than a clinically used taxane <i>in vivo</i>. Volvox can emerge as a powerful tool in the design of nanoscale supramolecular therapeutics for effective treatment of cancer