Molecular targeting health care through holistic network analysis

A common approach to molecular targeting therapy

Russ Penlington
Published in
7 min readJan 21, 2022


Molecular targeting healthcare methodology

A common approach to molecular targeting therapy focuses on the ability of a substance to inhibit or activate a particular gene or protein molecule.

This stems from the finding that the molecule is either over or underactive in a particular diagnosis. While the approach may provide therapeutic benefit, it can also disregard the effects on downstream molecules in complex molecular network structures. By accounting for the interactions between molecules in conjunction with the effects of introduced substances, a holistic approach to treatment plans can provide a more effective strategy including the countering to foreseen side effects.

The solo-target approach

In the most basic system, it is known that a particular molecular target is commonly either over or underactive in a particular diagnosis over a statistically significant majority of cases. In this example, we assume that molecule A is overactive and so experiments point to a certain substance 1 which has the most effect to inhibit the activity of the molecule.

FIG 1: substance 1 inhibits activity of molecule A.
FIG 1: substance 1 inhibits activity of molecule A.

The effectiveness of this scenario is, however, an overly simplistic representation of molecular activity since it ignores the knock effects that the treatment will have on related molecules.

The formula for calculating the effectiveness of this simple approach is the coefficient of how much substance 1 affects the activity of molecule A:

Effect(A) = Δ(1↓A )

Accounting for molecular networks

In reality, when one molecule is targeted to either increase or decrease its activity, it also creates a ripple effect on molecules that are dependent on its activity within molecular pathway networks. To illustrate this, we can assume a simple 3 molecule network with molecules A, B and C. Molecular networks are, of course, much more complex with many interactions across many targets that have crossovers on multiple networks. One analogy that can be made is that of a multiline train network that has multiple interchange points. Nevertheless, for illustrative purposes here we assume just one network with only 3 molecules and one substance 1.

FIG 2: substance 1 inhibits molecule A and activates molecules B and C.
FIG 2: substance 1 inhibits molecule A and activates molecules B and C.

Here we account for the effect of the substance on multiple molecules regardless of whether these molecules are of interest to the diagnosis in question. It is an important aspect for 2 main reasons:

  1. The effect of the substance on other molecules can help to identify potential side-effects (beneficial or adverse).
  2. The upstream and downstream relationships between molecules can help with optimizing the targeting strategy.

It is the second point that gives rise to the notion of network pathways that can play an important role in the treatment plan. So, the diagram needs to account for these.

FIG 3: substance 1 inhibits molecule A and activates molecules B and C. Molecule A activates molecule C, molecule C inhibits molecule B and molecule B inhibits molecule A.
FIG 3: substance 1 inhibits molecule A and activates molecules B and C. Molecule A activates molecule C, molecule C inhibits molecule B and molecule B inhibits molecule A.

The formula for the effect that substance 1 has on molecule A now becomes a function of all interactions.

Formula for all interactions
Formula for all interactions

We can break this down into the different pathway components to explain.

Interaction formula components
Interaction formula components

Note that by accounting for the upstream and downstream interactions we can also reduce the effect when the activity flows in the opposite direction of the desired interaction. These actions and counteractions are highly dependent upon the variations in affinity between substance 1 and each of molecules A, B, C as well as the affinities of each of the molecules A, B, C among each other before, during, and after interaction with substance 1. In other words, the magnitude of the affinities (as indicated by delta Δ) of the interactions between and among the various molecules exerts significant relative effects which also require an evaluation and clinical consideration. This is because the affinity deltas essentially change with duration, combinations of substances introduced, and diagnosis evolution.

Multiple substances on a network

Analyzing one substance in isolation fails to account for the reality of patient intake of other substances either naturally or prescribed for other ailments or side-effect management. This is an important aspect since, as we have identified, different substances have different effects on (and affinities among) molecular networks that can either antagonize or support the desired inhibition and activation effects on targets.

It is also important to note that substances are not limited to pharmaceutically prescribed medications but also natural health product supplements and dietary nutritional regimes. In fact, a molecular targeting approach can be accomplished entirely with natural health products, if preferred, which can also enable the introduction of more substances with less potential adverse side effects. Molecular targeting with the combination of natural health products and repurposed pharmaceuticals is, for example, a method employed by practitioners such as William LaValley MD at LaValley MD Protocols for molecular integrative oncology (MIO) cancer treatment plans.

In this case, we introduce three substances 1, 2, and 3 to the three molecular targets.

FIG 4: substances 1,2 and 3 have varying interaction types and coefficients on molecules A, B and C.
FIG 4: substances 1,2 and 3 have varying interaction types and coefficients on molecules A, B and C.

For simplicity, we omit the up/down interaction arrows as the arrow color indicates activation (green) and inhibition (red). The resulting effect is essentially the weighted sum of all substances relative impact on the molecular targets with some enforcing while others dampening the interactions on activity. The methodology can be used to model combinations of substances on molecular targets via pathways. The formula for this example is essentially the same as for substance 1 except we now add the additional substance with the same logic substituting 1 with 2 and 3. The long-form formula becomes exponentially more complex as we consider many substances and targets. For this reason, we can simplify the formula to:

Effect(MT) = Weighted Sum(Substance Direct Effect(MT) + Weighted Sum(Substance Indirect Effects(MT)))

Expanding to molecular pathway crossovers

When analyzing the indirect effects from molecular target interactions we must also account for the multi-dimensional aspect of network pathways and how they intersect through specific targets. This adds another layer of complexity to the calculation as indirect effects are in reality, not one-to-one, but many-to-many. The overall formula remains unchanged except that the combinations for indirect effects multiplies by all of the possible pathways that converge on the identified targets. Accounting for the complex pathways can have both canceling and reinforcing effects on the effectiveness of any of the substances.

Patient-specific genomics

For a molecular targeting treatment approach to be effectual, we must be able to quantify the interactions between substances and targets as well as among targets in both healthy and diagnosed conditions. This data is available, to differing degrees, from clinical research analysis publications and standardized in various online medical databases.

However, variations in molecular activity occur across patients depending on disease dynamics and unique genomic profiles. Therefore, a one size fits all treatment plan is suboptimal for any one particular patient case. Modern genomic profiling techniques can, however, provide information as to which molecular targets are likely either over or under active for a particular patient with a specific diagnosis. Combining this information with the multiple substance and target approach can provide improved optimization of treatment plan recommendations specifically tailored to the individual in order to maximize potential effectiveness.


A common approach to disease treatment is the development of marketing-approved medication by pharmaceutical companies understandably driven by profit motivation. These solutions are valid and can be effective for curing many diseases and/or symptoms thereof. In many cases, however, they are focused on single molecular targets and are therefore suboptimal in treating more complex diagnoses and disease processes. For these reasons, often additional medications are prescribed to widen the targeting scope and mitigate commonly encountered adverse side effects.

In expanding this approach by accounting for the holistic nature of molecular pathways, we can combine medications and other natural substances in a controlled manner to provide more effective, and patient-specific, treatment solutions.

“Let food be thy medicine and let thy medicine be food” (Hippocrates 440 BC)

Hippocrates is often quoted for advocating natural substances for health care; however, there is actually no evidence of him stating this in this way. In a literal translation, he did state that:

“In food excellent medicine can be found, in food bad medicine can be found; good and bad are relative.”

“I will apply dietetic and lifestyle measures to help the sick to my best ability and judgment; I will protect them from harm and injustice.”

The idea is, of course, now widely accepted scientifically that lifestyle including fitness and diet play an important role in our overall health and can also help in reversing disease progression when we become ill. Better understanding how these processes actually work at a molecular level unlocks additional tools and methods for us to implement them with greater successful healthcare outcomes.

About the Author

Russ Penlington is an independent consultant developing analytical and data science solutions for businesses and organizations. Russ’s original educational and career background is in corporate finance but has expanded into algorithm development for predictive analytics in a wider range of disciplines including financial markets and biotechnology. He is originally from New Zealand but now lives in France and works on projects across a variety of fields and locations.

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Russ Penlington

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