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A Probabilistic Diagram to Safer Chemical Design against Cytotoxicity

Honored Contributor
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Contributed by Longzhu Shen, Postdoctoral Associate, School of Forestry and Environmental Studies, Yale University

If one single factor has contributed most to the modernization of human lives, it would be chemicals because they are the building blocks of almost everything we eat, everything we wear, everything we use and everything that cures us. Without them, it is impossible to imagine the existence of civilization, humanity, and even life itself. However, these very same building blocks that ennoble us also gradually deteriorate our path toward sustainability.

The unintended biological activities or toxic effects associated with some commercial chemicals and chemical products pose risks to public health and the environment. For example, there have been well-known cases where certain chemicals caused the dramatic population decline of various wildlife species (e.g., DDT), and more recently, endocrine-disrupting chemicals have been shown to interrupt the developmental processes of aquatic species by acting as hormone mimics. These examples of adverse unintended consequences associated with chemicals have urged us to consider possible measures to better ensure human safety and ecological wellbeing.

One approach is to test for the potential toxic effects of chemicals on laboratory animals, such as rats, mice and dogs. However, these multi-tiered, multi-specie studies are expensive (millions of dollars per chemical) and generally require the sacrifice of a large numbers of animals. As a result, many chemicals are put on the market with minimal or no toxicity testing.

One important aspect of green chemistry is for researchers to implement a new approach where chemical products are designed to preserve efficacy of function while reducing toxicity. Though this sounds like a foolproof solution, tremendous barriers will need to be overcome before it can be realized. The most obvious is the general lack of training in toxicology among chemists. Therefore, the need to fill the knowledge gap by designing scientifically robust and easy-to-use tools that guide chemists to deign chemicals with reduced toxicity potential becomes evident.

Towards this objective, I have focused my research on cytotoxicity (the quality of being toxic to cells) as the target point to develop a design algorithm. I obtained in vitro chemical toxicity data from the U.S. EPA Toxicity ForeCaster (ToxCast). Then, I integrated computational quantum chemistry and statistical learning algorithms to build a model that can be used to inform the design of chemicals to deliver a customized probability that the proposed chemical will not exert cytotoxic effects. That is, starting from a desired probability that a chemical will not exert a specific toxic effect, one can simultaneously explore multiple chemical properties in chemical property space to seek the complete solution for chemicals that can meet such a desired probability. I will use an example to illustrate how to design a chemical with a targeted probability where the molecule will not be cytotoxic. As shown in the figure below, a sample solution is represented by the dotted lines. The goal is to seek solutions in the chemical space constituted by the three black chemical property axes that satisfies an 82 percent probability (purple axis) that the compound will not be toxic.

Cytotox_diagram.pngTo start, we chose the nearest polarizability (PLRZ) axis.  For instance, polarizability needs to be around 12 to meet functional requirements in certain industries. We then connect the two points on the probability (82) and PLRZ (12) axes to arrive at a specific point on the auxiliary (R1) axis.

Now multiple options reveal the combination of logP (water-octanol partition coeffcient) and SOF (molecular softness). Assuming for certain industrial application needs, logP is required to be near 2.8. Then, we need to connect the dots from R1 to logP at 2.8, and the line naturally extends to SOF at 0.105. Alternatively, if we have information about SOF ahead of time, we can locate a specific point on the SOF axis and link the anchor point on R1 to SOF directly. The value of logP can be determined along the way.

The resulting molecule corresponds to propylparaben, a naturally occurring chemical that has been approved by the U.S. Food and Drug Administration (FDA) for use in cosmetics. Of course, other solutions are possible when one makes different choices at possible points on the black axes.

This plot allows the researcher to find all the possible solutions in the current design variable space that, at any desired probability, a proposed chemical is non-cytotoxic. This initial fruitful outcome in the molecular design study is expected lead to more comprehensive/complicated diagrams in the future.

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