Title:Predicting the Efficacy of Novel Synthetic Compounds in the Treatment of
Osteosarcoma via Anti-Receptor Activator of Nuclear Factor-κB Ligand
(RANKL)/Receptor Activator of Nuclear Factor-κB (RANK) Targets
Volume: 20
Issue: 7
Author(s): Wenhua Zhang, Siping Xu, Peng Liu, Xusheng Li*, Xinyuan Yu and Bing Kang
Affiliation:
- The 940th Hospital of Joint Logistics Support force of Chinese People's Liberation Army, Gansu, China
Keywords:
Osteosarcoma, tumor targeting agents, QSAR, GEP, drug design, HM algorithm.
Abstract:
Background: Osteosarcoma (OS) currently demonstrates a rising incidence, ranking as
the predominant primary malignant tumor in the adolescent demographic. Notwithstanding this
trend, the pharmaceutical landscape lacks therapeutic agents that deliver satisfactory efficacy
against OS.
Objective: This study aimed to authenticate the outcomes of prior research employing the HM and
GEP algorithms, endeavoring to expedite the formulation of efficacious therapeutics for osteosarcoma.
Methods: A robust quantitative constitutive relationship model was engineered to prognosticate the
IC50 values of innovative synthetic compounds, harnessing the power of gene expression programming.
A total of 39 natural products underwent optimization via heuristic methodologies within the
CODESSA software, resulting in the establishment of a linear model. Subsequent to this phase, a
mere quintet of descriptors was curated for the generation of non-linear models through gene expression
programming.
Results: The squared correlation coefficients and s2 values derived from the heuristics stood at
0.5516 and 0.0195, respectively. Gene expression programming yielded squared correlation coefficients
and mean square errors for the training set at 0.78 and 0.0085, respectively. For the test set,
these values were determined to be 0.71 and 0.0121, respectively. The s2 of the heuristics for the
training set was discerned to be 0.0085.
Conclusion: The analytic scrutiny of both algorithms underscores their commendable reliability in
forecasting the efficacy of nascent compounds. A juxtaposition based on correlation coefficients
elucidates that the GEP algorithm exhibits superior predictive prowess relative to the HM algorithm
for novel synthetic compounds.
