PTML Model for Selection of Nanoparticles, Anticancer Drugs, and Vitamins in the Design of Drug–Vitamin Nanoparticle Release Systems for Cancer Cotherapy

Nanosystems are gaining
momentum in pharmaceutical
sciences because of the wide variety of possibilities for designing
these systems to have specific functions. Specifically, studies of
new cancer cotherapy drug–vitamin release nanosystems (DVRNs)
including anticancer compounds and vitamins or vitamin derivatives
have revealed encouraging results. However, the number of possible
combinations of design and synthesis conditions is remarkably high.
In addition,
a large number of anticancer and vitamin derivatives have been already
assayed, but a notably less number of cases of DVRNs were assayed
as a whole (with the anticancer compound and the vitamin linked to
them). Our
approach combines with the perturbation theory and machine learning
(PTML) model to predict the probability of obtaining an interesting
DVRN by changing the anticancer compound and/or the vitamin present
in a DVRN that is already tested for other anticancer compounds or
vitamins that have not been tested yet as part of a DVRN. In a previous
work, we built a linear PTML model useful for the design of these
nanosystems. In doing so, we used information fusion (IF) techniques
to carry out data enrichment of DVRN data compiled from the literature
with the data for preclinical assays of vitamins from the ChEMBL database.
The design features of DVRNs and the assay conditions of nanoparticles
(NPs) and vitamins were included as multiplicative PT operators (PTOs)
to the system, which indicates the importance of these variables.
However, the previous work omitted experiments with nonlinear ML techniques
and different types of PTOs such as metric-based PTOs. More importantly,
the previous work does not consider the structure of the anticancer
drug to be included in the new DVRNs. In this work, we are going to
accomplish three main objectives (tasks). In the first task, we found
a new model, alternative to the one published before, for the rational
design of DVRNs using metric-based PTOs. The most accurate PTML model
was the artificial neural network model, which showed values of specificity,
sensitivity, and accuracy in the range of 90–95% in training
and external validation series for more than 130,000 cases (DVRNs
vs ChEMBL assays). Furthermore, in the second task, we used IF techniques
to carry out data enrichment of our previous data set. In doing so,
we constructed a new working data set of >970,000 cases with the
data of preclinical assays of DVRNs, vitamins, and anticancer compounds
from the ChEMBL database. All these assays have multiple continuous
variables or descriptors dk and categorical variables cj (conditions of the assays) for drugs (dack, cacj), vitamins (dvk, cvj), and NPs (dnk, cnj). These data include >20,000 potential
anticancer compounds with >270 protein targets (cac1), >580 assay cell organisms (cac2), and so forth. Furthermore, we include >36,000
assay vitamin derivatives in >6200 types of cells (c2vit), >120 assay organisms (c3vit), >60 assay strains (c4vit), and so forth. The enriched data set also contains >20 types
of DVRNs (c5n) with 9 NP core materials
(c4n), 8 synthesis methods (c7n), and so forth. We expressed
all this information with PTOs and developed a qualitatively new PTML
model that incorporates information of the anticancer drugs. This
new model presents 96–97% of accuracy for training and external
validation subsets. In the last task, we carried out a comparative
study of ML and/or PTML models published and described how the models
we are presenting cover the gap of knowledge in terms of drug delivery.
In conclusion, we present here for the first time a multipurpose PTML
model that is able to select NPs, anticancer compounds, and vitamins
and their conditions of assay for DVRN design.