Which chemotherapeutic agent was the most effective

However, chemotherapy is actually a broader term that refers to any use of chemicals or drugs to treat disease. Chemotherapy may involve drugs that target cancerous cells or tissues, or it may involve antimicrobial drugs that target infectious microorganisms. Antimicrobial drugs typically work by destroying or interfering with microbial structures and enzymes, either killing microbial cells or inhibiting of their growth.

Figure 1. For millennia, Chinese herbalists have used many different species of plants for the treatment of a wide variety of human ailments. Although the discovery of antimicrobials and their subsequent widespread use is commonly associated with modern medicine, there is evidence that humans have been exposed to antimicrobial compounds for millennia.

Chemical analyses of the skeletal remains of people from Nubia [1] now found in present-day Sudan dating from between and AD have shown residue of the antimicrobial agent tetracycline in high enough quantities to suggest the purposeful fermentation of tetracycline-producing Streptomyces during the beer-making process. The resulting beer , which was thick and gruel-like, was used to treat a variety of ailments in both adults and children, including gum disease and wounds.

The antimicrobial properties of certain plants may also have been recognized by various cultures around the world, including Indian and Chinese herbalists Figure 1 who have long used plants for a wide variety of medical purposes. Healers of many cultures understood the antimicrobial properties of fungi and their use of moldy bread or other mold-containing products to treat wounds has been well documented for centuries.

Figure 2. Paul Ehrlich was influential in the discovery of Compound , an antimicrobial agent that proved to be an effective treatment for syphilis. Societies relied on traditional medicine for thousands of years; however, the first half of the 20th century brought an era of strategic drug discovery. In the early s, the German physician and scientist Paul Ehrlich — set out to discover or synthesize chemical compounds capable of killing infectious microbes without harming the patient.

Compound was found to successfully cure syphilis in rabbits and soon after was marketed under the name Salvarsan as a remedy for the disease in humans Figure 2.

A few decades later, German scientists Josef Klarer , Fritz Mietzsch , and Gerhard Domagk discovered the antibacterial activity of a synthetic dye, prontosil , that could treat streptococcal and staphylococcal infections in mice.

Gerhard Domagk — was awarded the Nobel Prize in Medicine in for his work with prontosil and sulfanilamide , the active breakdown product of prontosil in the body. Sulfanilamide, the first synthetic antimicrobial created, served as the foundation for the chemical development of a family of sulfa drugs.

A synthetic antimicrobial is a drug that is developed from a chemical not found in nature. Springer Berlin Heidelberg ; : p. Nature Reviews Cancer. Open in Read by QxMD. S phase.

Neoplastic conditions Breast cancer Head and neck cancers e. Cytarabine arabinofuranosyl cytidine. Leukemias e. Systemic treatment Breast cancer Gastric cancer Colorectal cancer Pancreatic cancer Topical treatment Actinic keratosis Basal cell carcinoma.

Acute lymphoblastic leukemia Immunosuppression Organ transplants Autoimmune diseases e. Chronic lymphocytic leukemia CLL Myeloablation prior to hematopoietic stem cell transplant. Hairy cell leukemia Multiple sclerosis. Hydroxyurea hydroxycarbamide. Myeloproliferative disorders e.

Neoplastic conditions Solid tumors e. Solid tumors e. Multiple myeloma Ovarian cancer Amyloidosis. Glioblastoma Anaplastic astrocytoma. Carmustine Lomustine Streptozocin.

Myeloablation prior to hematopoietic stem cell transplant. Hodgkin lymphoma Brain tumors e. Platinum-based agents.

Cisplatin Carboplatin Oxaliplatin. S and G2 phase. Colorectal cancer. S and M phase. Docetaxel Paclitaxel. M phase Late G2 mitotic phase. Nontaxane microtubule inhibitors. Breast cancer Liposarcoma.

Ixabepilone Epothilone. M phase. Breast cancer. G2 phase. Squamous cell carcinomas of the head and neck Testicular cancer Hodgkin lymphoma Malignant pleural effusion. Actinomycin D. Childhood tumors Wilms tumor Ewing sarcoma Rhabdomyosarcoma Gestational trophoblastic neoplasia.

Anthracyclines Doxorubicin Daunorubicin Idarubicin. Solid tumors Leukemias Lymphomas Hodgkin and non- hodgkin lymphomas. Palliative chemotherapy of gastric and pancreatic cancer Bladder cancer.

Imatinib Dasatinib Nilotinib. EGFR tyrosine kinase inhibitors. Erlotinib Gefitinib Afatinib Osimertinib. Alectinib Crizotinib. G0 and G1 phase. Dabrafenib Vemurafenib Encorafenib. MEK inhibitors. Bruton tyrosine kinase inhibitors. Ibrutinib Acalabrutinib. G1 phase. Janus kinase inhibitors.

Anthracyclines: Anthracyclines are anti-tumor antibiotics that interfere with enzymes involved in copying DNA during the cell cycle. They bind with DNA so it cannot make copies of itself, and a cell cannot reproduce. Enzymes are proteins that start, help, or speed up the rate of chemical reactions in cells. They are widely used for a variety of cancers. A major concern when giving these drugs is that they can permanently damage the heart if given in high doses.

For this reason, lifetime dose limits also called cumulative dose are often placed on these drugs. These drugs are also called plant alkaloids. They interfere with enzymes called topoisomerases , which help separate the strands of DNA so they can be copied.

Enzymes are proteins that cause chemical reactions in living cells. Topoisomerase inhibitors are used to treat certain leukemias, as well as lung, ovarian, gastrointestinal, colorectal, and pancreatic cancers. Mitotic inhibitors are also called plant alkaloids. They are compounds derived from natural products, such as plants. They work by stopping cells from dividing to form new cells, but can damage cells in all phases by keeping enzymes from making proteins needed for cell reproduction.

They are used to treat many different types of cancer including breast, lung, myelomas, lymphomas, and leukemias. These drugs may cause nerve damage , which can limit the amount that can be given.

Corticosteroids, often simply called steroids , are natural hormones and hormone-like drugs that are useful in the treatment of many types of cancer, as well as other illnesses. When these drugs are used as part of cancer treatment, they are considered chemotherapy drugs. Whether this is true will depend on how the characteristics of the sensitive population change in response to combination therapy S10 Text.

Conversely, if mutagenic antimicrobials and anticancer drugs must be used, it may be better to use them aggressively unless resistant cells are already at high densities. We analysed our conceptual framework Fig 1 using a specific model of the process Eq 3.

This specific model made a number of key assumptions. The first is that the dynamics of the target cells at any particular time depend only on the time and the densities of the target cells at that time. This assumption allowed us to conclude that maximizing the sensitive density will be advantageous whenever its immediate effect is to decrease the resistant expansion rate S7 Text with S5 Fig. This assumption will not hold if the pathogen densities experienced by a patient during earlier stages of the infection impact the pathogen dynamics during later stages of the infection.

This can occur if, for example, the rate at which protective immunity develops depends on antigen load rather than simply time, as we assumed. Related issues arise if resource replenishment in the patient depends on past pathogen densities.

Although these scenarios require a separate detailed analysis, some insight into the likely outcomes in such situations can be made by applying the general principles outlined here S8 Text. A second key assumption is that competition is modelled with a basic logistic formulation, in which the competitive impact of a given cell is unlinked to its resistance phenotype. In S9 Text , we consider more complex situations in which intra- and inter-strain competition differs and depends on resistance phenotype.

We also consider the case of Gompertz competition, more frequently considered in models of cancer [ 34 , 38 ]. Although the mathematical details differ, both of these alternative competitive formulations generate threshold conditions analogous to Eqs 4 and 5 , which also depend on the biology of the patient, target cells, and drug.

There is considerable potential for further theoretical analysis. For instance, we have modelled the ecological and mutational processes deterministically, but when resistance is very rare, stochastic processes will become important [e.

Similarly, we considered just two extreme treatment options immediate removal of the sensitive population or containment of the entire cell population at the acceptable burden. There are other possibilities. Complexities are also introduced by relaxing the assumption that resistance is an all-or-nothing trait. Resistance that renders cells impervious to treatment remains the primary clinical concern, but if several mutational steps are required to full resistance, this will introduce history dependence to the mutational processes S10 Text.

Likewise, if resistance can be acquired through horizontal gene transfer, then things can become a great deal more complex depending on whether the resistance comes from the resistant population or from other species in the microbiota which may nor may not be impacted by drug treatment S10 Text. It may also be interesting to explore the impact of more complex assumptions about immunity S10 Text. We also note that our analysis of the potential clinical gains of containment Fig 3 is specific to the particular model formulation Eq 3 ; although we expect the general trends to be similar for other formulations, the quantitative predictions will be different.

Clearly, there are situations where there is no acceptable burden e. We note, however, that there is abundant justification for the idea of an acceptable burden in nonsterile site infections asymptomatic bacteriuria, gastrointestinal bacteria. Exactly what constitutes a maximum acceptable burden is likely to be a very complex problem, which will depend on numerous factors that have to be carefully considered.

In the meantime, for this proof-of-principle analysis, we simply postulate that such a burden exists. We note that we are not alone in assuming this. For cancers, the concept of adaptive therapy [ 32 , 33 , 45 ] also rests on the assumption that there is an acceptable burden.

In infectious diseases, tolerance or antidisease drugs are actively being investigated, usually as possible solutions to the resistance problem [ 98 — ]. Moreover, there are contexts in which adding drug-sensitive microbes is actively under consideration e. The fundamental premise of these approaches is that the resistance management benefit of drug-sensitive microbes may have much to offer clinically. Finally, we note that our approach has been patient-centred.

In infections, an additional concern may be the spread of resistance from patient to patient. Our approach may be adapted in this case by redefining the acceptable burden to be that which reduces transmission to an acceptable level. Even for the simpler cases considered here Eq 3 , several practical hurdles need to be overcome before resistance management gains can be attained from regimens aimed at containment.

Most of the key parameters Eq 4 are defined by biological properties of the system and are thus likely to generalise across classes of patients, but one critical and highly patient-specific parameter is the resistant density at the start of the management period. In practice, resistant cells will frequently be undetectable when initial treatment decisions need to be made.

It might still be possible to generalise in the absence of patient-specific data e. Moreover, our analysis also provides no guidance on the specific treatment regimens required to achieve containment.

One option is adaptive therapy [ 33 ], in which drug dosing and inter-dose intervals are progressively adjusted in response to measurements of the tumour or infection burden. Recent studies have shown that this is possible in at least some clinically relevant settings e.

Note that the effectiveness of containment will be maximized by keeping the tumour or infection biomass at the allowable burden, but gains will continue to accrue provided the biomass is maintained within a defined range see S11 Text.

Thus, from a practical perspective, it is not necessary to keep the total cell population at precisely the acceptable burden. This allows for greater flexibility when implementing containment.

It is tempting to think that containment might be worth trying whenever conventional aggressive chemotherapy is virtually certain to fail due to resistance [ 33 , 45 ], as it is in many cancers. We note, however, that an important conclusion from Eq 5 is that even when the prognosis for aggressive treatment is not good, there will be situations where attempting to contain the tumour or infection will make things even worse. Thus, patients enrolled in clinical trials of containment strategies need to be chosen carefully.

Containment strategies should be first attempted where acceptable burdens are relatively large and easily measured. An attractive possibility is to first investigate containment in patients where the side effects of aggressive chemotherapy can be profound e.

It might also be worth trying in situations where aggressive treatment has failed and no alternative drugs are available. Unless all sensitive cells were removed in the initial bout of chemotherapy, such a situation might accord with Fig 2B and prolong life.

Decades of experience in agriculture has led to the belief that the often rapid loss of once highly effective insecticides, pesticides, herbicides, and fungicides can be slowed and even halted if chemicals are used to contain rather than eradicate pest species.

That paradigm, widely accepted in agriculture [ , ], has yet to be seriously investigated in medicine [ 32 ]. Our analysis makes clear that there are situations where containment may lead to clinical gains.

It also reveals that there are situations where current standard practice, even when it fails, will fail more slowly than a containment strategy. One issue that we have not considered is the intriguing possibility that containment may select for cells that are best able to compete in chronically controlled populations. These might more effectively contain resistant competitors and might themselves have rather low replication rates.

Should such evolution occur—and there are suggestions it might [ 33 , 45 , ]—this would be a further argument for investigating chemotherapeutic strategies aimed at containing the target population rather than eliminating it. Panel A: The dynamics of the resistant density under containment dashed red and aggressive treatment solid red. The points A and B indicate the resistant density R 1 0 on the containment curve and the aggressive treatment curve respectively.

This shows the dynamics of the resistant density under containment dashed red and aggressive treatment solid red when the starting resistant density is R 1 0. Because the aggressive treatment curve was shifted more than the containment curve the two curves now intersect below the acceptable burden.

There are two steps involved in obtaining the actual resistance dynamics from these curves. Panel B: Step One. Panel C: Step Two. The rate of change of the actual containment curve black dashed will be greater than the one shown in Panel B i. This is because the immune response of the shifted curve will be less.

This difference will increase in time. This is also true for the aggressive treatment curve black solid , but the difference will be greater because the aggressive treatment curve involved a larger shift in time.

Because the aggressive treatment curve was shifted more than the containment curve the two curves now intersect at an even lower resistant density point C 3 is below point C 2. The black horizontal lines indicate the distance between the containment curve and the aggressive treatment curve at different resistant densities. Notice that the black horizontal lines in Panel B are shorter than the corresponding lines in Panel A. This indicates that accounting for the fact that the immune function is a non-decreasing function of time actually decreases the distance between the containment and aggressive treatment curves.

This means that they will intersect at a lower resistant density. The starting resistant density exceeds the balance threshold. The starting resistant density is below the balance threshold. The minimizing regimen chooses the sensitive density that minimizes the resistant expansion rate at each instant in time red curve.

This curve will never exceed the curve resulting from any other alternative strategy for example, the black curve. In both cases the curve corresponding to the minimizing regimen red curve is driven below the alternative curve black curve. We thank D.

Kennedy and other members of the Read and Thomas groups as well as T. Day, D. Hughes and R. Hohl for discussion. Received by AFR. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Eberly Family. National Center for Biotechnology Information , U. PLoS Biol. Published online Feb 9. Robert J. Andrew F. David Schneider, Academic Editor. Author information Article notes Copyright and License information Disclaimer.

Received Sep 14; Accepted Jan 6. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This article has been cited by other articles in PMC.

S3 Fig: Magnified version of the curves in S2 Fig. S4 Fig: Ratio of time to treatment failure under containment to time to treatment failure under aggressive treatment.

S5 Fig: Minimizing the resistant expansion rate at each instant in time will maximally delay treatment failure. S2 Text: Standard logistic formulation. S3 Text: Derivation of the balance threshold.

S4 Text: Derivation of Eq 5 from main text. S6 Text: Supporting calculations for Fig 3 of main text. S7 Text: Minimizing the resistant expansion rate will maximally delay treatment failure. S8 Text: Slowing resistance emergence: immediate versus future effects of the sensitive population. S9 Text: Alternative ways to model competition. S10 Text: Further complexities. S11 Text: Containment can be effective even if the total pathogen density is below the acceptable burden.

S12 Text: Supporting calculations for Fig 4 of main text. Abstract When resistance to anticancer or antimicrobial drugs evolves in a patient, highly effective chemotherapy can fail, threatening patient health and lifespan.

Author Summary When resistance to anticancer or antimicrobial drugs evolves in a patient, highly effective chemotherapy can fail, threatening patient health and lifespan.

Results Conceptual Framework For clarity, we present our analysis in the context of a hypothetical infection, but this framework can also be applied to cancer. Open in a separate window. Fig 1. Generic model of infection under aggressive treatment A and containment B. Mathematical Framework The dynamics of a pathogen population are determined by a time-varying combination of pathogen removal and replication. Under these assumptions, the expansion rate of the resistant density R in a purely resistant infection can be described by a basic logistic growth equation S2 Text R.

Aggressive Treatment or Containment? Fig 2. Schematic comparing aggressive treatment to containment. Clinical Gains The above analysis defines the situations where containment delays treatment failure longer than aggressive treatment, but in these situations, how much more effective is containment?

Fig 3. Ratio of duration of management period under containment to duration of management period under aggressive treatment. Discussion For many infections and cancers, resistance emergence is a major determinant of health outcomes. Theoretical Development There is a long history of mathematical modelling of resistance in both infections and cancer [ 14 , 23 , 38 , 91 — 96 ].

Box 1. Additional Approaches Competition and growth modifiers: Recently, a number of therapies have been developed that inhibit pathogen or cancer cell proliferation rather than directly killing cells [e.

Fig 4. The impact of alternative therapies. Outlook Decades of experience in agriculture has led to the belief that the often rapid loss of once highly effective insecticides, pesticides, herbicides, and fungicides can be slowed and even halted if chemicals are used to contain rather than eradicate pest species. PDF Click here for additional data file.

S3 Fig Magnified version of the curves in S2 Fig. S4 Fig Ratio of time to treatment failure under containment to time to treatment failure under aggressive treatment. S5 Fig Minimizing the resistant expansion rate at each instant in time will maximally delay treatment failure.